Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore

The thing that I find the most useful about, like, these models in general is, like, when I have these big spreadsheets of a lot of results and I just need plots of it. I think models can quite good at screenshotting a plot of this. I hate making this method, like, stuff about it. It's so annoying. There were so many moments this year where AI s- suddenly crossed that, like, the emergent thing. Like the AI coding as one of them, like we just discussed.

I think, like, Nano Bana also got to the point where I usually, like, you make these images, it's just like doing it for fun and just tr- troll your friend or something like that. But like Nano Bana actually really got so good. [upbeat music] Welcome back. Yeah. How are you? Yeah, I'm good. I'm good. Great to be back. It's been one, one and a half years. Yeah, it's been one and a half years.

Feels like a long time. So last time we talked, you were at Reica. Yeah. And then you joined GDM again, working for Quark again. Yeah. And more recently, you've started GDM Singapore. Yeah. Is it GDM Singapore or Gemini Singapore? I don't know if you've named the team. Oh, I, I, I think we have a Gemini team in Singapore, yeah. Gemini team in Singapore. It's called Reasoning and AGI.

Yeah, Reasoning and AGI. Is it important to have AGI in the name? I think it was like a vibes thing that we put AGI in. Yeah, I think that, like, one reason why we work on these models is that we want to get to AGI, and I just, it was a vibes thing that we added the AGI to the job posting, yeah. There is no, like, formal name of the team yet- Team yet...

but it's basically the Gemini team Singapore, yeah. I mean, I think people are, like, trying to triangulate. Amazon has an AGI team, you guys have an AGI team, and then let's say Meta now has a super intelligence team. What are people signaling when they choose these names for their teams? Do they have, "Oh, we have a plan," or is it just vibes? [laughs] Are you trying to fish some hot takes from me? No.

Uh- [laughs] You have officially AGI in your job title. No, it's not a team name. It's, it's not a team name we have. Yeah, it's just, you know, we, we just want to signal the north star of we are building these models to get to AGI. Yeah. Yeah. No, I, I wasn't really fishing for hot takes. Okay, so you rejoined GDM. Yeah.

And I think last time when you talked about, I listened back to the whole thing, it was an amazing episode last time. You were talking about how it's like externally, you were in Brain and came out, and now you're back in GDM. Yeah. I wonder what's just your general reflections just plugging back into the Google infrastructure.

Oh, yeah. So I guess coming back is very interesting because it felt, and return to Google, like, everything, including your LDAP, your username is all the same. It's like you play Pokémon, you, you leave it aside, and then you go back and you click continue game. You save game. Yeah, you save game and then continue game. It's like that.

Obviously, the last one point five years while I was away, many things have changed. Brain is now part of GDM and stuff, so I think that obviously a lot of things have changed. But I think overall, the coming back has been pretty seamless. Obviously, I, I love Google infrastructure, and I think debuts are great and stuff like that. Yeah, and I'm very glad to, to be back to- Yeah...

to Google Infra, yeah. And was the intention always that you were going to work on Deep Think? No, not really. I think I miss research a lot, like doing, like, research. Not like super fundamental research but, like, close to model research, right? But I really miss being at the frontier and trying to s- go be-beyond that, right?

So I really miss that a lot, and I think when I came back, Deep Think wasn't a thing, and I don't think there was any plans, actually. It was just like, I'm just going to work on research and see what happens. Yeah. I'm sure, I guess, there was some inclination that reasoning is the next frontier, and that's, like, obviously the most rewarding research path, especially this year.

[laughs] Yeah, I think reasoning, these days, reasoning and RL is, like, probably quite-- is RL reasoning comes up. I spent a lot of my past life, I call it the past arc, working on, like, architectures and pre-training. But I think now I more-- I would like transition more into RL research. I'm not like old school RL, but the games RL and the old school RL. And, and to be honest, I had almost no RL background coming back.

But I think, like, like RL is the main means of modeling these days, and yeah. So I think it was pretty easy to jump back in. And I think a lot of fundamental skills in research is general purpose and universal, and, and it's, it's quite easy to innovate even in a tool set that you're not super used to.

And yeah, so I think RL is basically the main modeling tool set that we play around with- Yeah... these days. Superficially, I see some, you know, in your UL2 and Fan-- uh, T5 work, um, so some overlap of like, you know, the, the focus on objectives and the focus on the stuff that you're trying to incentivize.

So I would have maybe guessed there was more overlap than you're saying right now, which is interesting. [laughs] But I know-- I understand it's very superficial. Basically, our objective is objective, and they have some, like, overlap, right? Yeah. I think it's just mainly, like, the on-policy and off-policiness of designing these things that change how, like, also the learning algorithm itself, right?

Let's just introduce this kind of terminology to people if they're not that familiar with the sort of RL policy. I do think that a lot of people are, like, trying to understand what is working about this generation of RL research. Anyway, so Jason had this interesting post, which I think you were co-signing, which is basically you always want to be on policy.

Instead of mimicking other people's successful trajectories to your own actions and learn from the reward given by the environment, basically correct your own path instead of trying to imitate other people's path. Yeah, yeah. And first of all, he writes really well, and I wish that more people wrote like him. But I don't know, what's your reflection on that or your addition on top of that?

Yeah, so I think, like, the biggest analogy of on-policy and off-policy is basically that on-policy is basically like when you SFT something, it's on-policy. Basically, you take some other model, larger model stuff, and then it's basically like this off-- somebody else's generated outputs, trajectories, and whatever.

I think on-policy is mainly like the core idea of like modern LM RL, where you, like, generate and then you reward the model based on its own generations, and then the model trains on its own generations. Yeah. So it's more-- It's a bit like- Self, this distillation to some extent, you, the model generates its own output, and then you reward it and then trains on its own output.

So I think on-policy learning is basically this idea of, like, model training on its own outputs and letting the model, like, generate its own trajectories and then let, letting some reward verify it, and then the model train its own outputs. I think this is more generalizable in general. I think there's still a lot of, like, science out and still to be done about the gap between SFT and, and RL itself, but I think basically on-policy and off-policy, right?

And I think bringing this analogy back to real, like, life, I mean, we-- this on-policy learning is more like, like, humans, we are more on-policy because we go around the world, we make mistakes, and then we are, "Okay, this is..." But, like, imitation learning is mostly somebody else- Not first principle. They just copy... they just tells you what to do and then you just copy. Yeah.

So I think, yeah, this philosophy, bringing back this philosophy to life is quite, like, powerful. Like, when I-- like, n-now I have a kid and everything, like, want my kid to try stuff and then you tell them like, "Okay, this is, like, where this went wrong, where this went right," and stuff rather than, "Okay, you just copy everything somebody else does."

Yeah. There's a-- Montessori schooling is mostly that, right? Like, very unstructured learning. Like, you discover your own path, and we just give you a safe environment to do it. Yeah, yeah, yeah, yeah. What is the point at which you should transition from imitation to on-policy? I do bounce back and forth. For humans, right, not models, right?

I, I would say in models it seems like there mostly has been a very concrete, like, first you imitate, and that's pre-training, and then you are out here. I mean, technically SFT is still imitation, but I think for humans as well a little bit of this, right? Because if you basically, like, it's like sports, right?

When you play sports, you start off by imitating, like, hardcore imitating. But then you cannot imitate forever because you need to... Like, imitation, I, I don't know whether this is a good analogy, but watching a lot of tutorials and stuff is more like imitating. We learn, try to learn certain movements and stuff like that.

But then, like, on-policy learning is like going to the game itself and trying to get a reward signal from that, right? But so I think that humans do need some form of imitation learning, but, like, I think everybody starts off by imitating. But then again, the human and model kind of is not-- it, it's just fun to have analogies, but we shouldn't, like, take things, like, super literally and stuff like that. I actually am a quite a serious taker of machine learning insights into human learning. That's how we learn from our models now? Yeah. Hmm.

Because I think, like, machine learning is the most scientific way we have ever studied learning, just in general. [laughs] And that's true. That's true. Where we had to invent curriculum from, like, scratch. Yeah, that's true. [laughs] And things like learning rate. If your learning rate is too high, blah, blah. Learning rate is too low, blah, blah. Like, wait, do humans even have a learning rate?

So I do tell people to, to keep an idea of their own learning rate and to be wary of it being too low. So for example, if you've been wrong once, you should ask, "Where else have I been wrong?" And typically, usually, l-let's say learning- Oh, okay... you know what I mean? People usually update slower than they should when they have been wrong. Well, is it stubbornness? [chuckles] It could be stubbornness.

Um, I don't know. Is that, is that the right word for it? It could be like they are, they're too Bayesian when actually it sh-- like, their prior assumptions are wrong, and they need to completely throw out their previous assumptions because one counterexample invalidates all prior experience. Your entire world model is wrong. Throw it away. So Bayesian are actually wrong. Let's say you live for ten years under some assumptions, and you have one example that breaks your narrative. Okay.

You shouldn't be like, "Okay, now I have two percent update." No, actually, you should be like, "Oh," like, "something's really freaking changed. Everything I've assumed for the last ten years is probably wrong. What else am I wrong in?" And update twenty percent, update fifty percent, not two percent. You know what I mean? That's your-- that's a learning rate thing for me.

So my, my direct example is the whole getting into AI stuff. I was watching GANs for ten years. Yeah. Has it been ten years? Uh, twenty twelve, twenty thirteen. Time flies, yeah. [laughs] I was watching GANs and I was like, "Okay, this is cool. It's getting more detail. Not that impressive." Then all of a sudden, stable diffusion came out, and you can run it on your laptop, and that was my learning rate. "Okay," like, "fuck." Like, my mental model of generative image, images did not include this.

And so I was like, "Okay," like, "I am very wrong and I need to pivot everything." And that's how I started Latest Space. So would this mean that, like, your, your learning rate is high? Yes. I, yeah, I will nudge it up. I, I schedule my learning phase- [laughs]... because a world model has been violated. Okay. I think it's a good, it's a good strategy.

I think also this brings a little bit to, like, when new paradigms happen, like how fast people are to adopt it or, like, to invalidate their understanding of things. I think as scientists, we def-definitely, a lot of times we do have to keep-- as the field progress, we do have to keep, like, invalidating our own world model.

It could be, like, a certain way is the way to do s-like something all along, and suddenly something comes along and invalidates it. Yeah. Yeah. Yeah. You can be very proud of your priors until it's, like, becomes your prison. Yeah, I know. Yeah, yeah. [laughs] That is, like, actually very dangerous. Yes, it is. Yeah. Okay. That was a bit of a tangent.

I don't know how we got there. You did highlight Danny's LLM reasoning lectures where he kind of traced the intellectual history of reasoning in LLMs. Yeah, yeah. Chain of thought to, to now RLFT. And then the, uh, one part that I was gonna prompt you a little bit was also self-consistency, right? Yeah. Which I think people roughly know. I think it's more crudely implemented with OpenAI than with you guys, where it is straight up they have eight inferences and they- Yeah... they judge or whatever.

But I do think that also is relevant to on-policy distillation, where it's like literally you, you have eight different paths and y- they're all from the same model. Is it-- So I'm checking my intuition there. Basically, the stuff that you were saying about why on-policy is important and using, let's say, an external verifier to, to improve your reasoning, you can also do that with parallel reasoning. Oh, yeah, yeah.

I mean, like, when we train our models, they sample multiple times. So yeah, to some extent there's some form of- Yeah... self-consistency. Is, is that directly-- That's self-consistency, right? Yeah. Self-consistency is a little bit more- Budging... it's a more nuanced version of-- If you talk to Danny, he would tell you it's not majority voting for sure, but it's, it's more an- I agree. I agree.

Yeah, it's more nuanced version of that, but I think parallel thinking definitely is related to self-consistency. Yeah. Yeah. I think for those people-- OpenAI also actually put out some interesting papers on majority voting versus other forms of, like- Multiple output consensus. Then basically like you, like the highest level is actual, an actual LLM judge that decides like, this is actually a worthwhile trajectory that is more valid based on some internal consistency or just like inspecting the chain of thought.

Which is kind of very cool that we can chain models to do that. Yeah, for sure. Yeah. Yeah. I-- self-consistency is a big, like a big fundamental idea in-- I mean, chain of thought itself was also a big idea, and then self-consistency was also like a big fundamental idea in, in, in, uh, in modern like LLM- Mm.

uh, literature. Yeah. Amazing. Okay. So let's bring it to, I guess, o-o-- the-- one of the headlines of this podcast is going to be about diving into the IMO work. Yeah. So this was around about May, March. March, uh, in July? July. You guys announced-- oh, this very nice photo here. This is the photo I was looking at. This is in London, I believe, where you had Dishoom. Yeah, Dishoom.

[laughs] Oh, you got to be at a, like you got to be at a photo taking to, to get the credit. [laughs] That's bullshit, right? What the fuck? [laughs] No, no, no, no. No, I'm just kidding. Well, the contributor list is bigger than this. Y-yeah, yeah. But like, they were like saying that, "Oh, like, okay, you, you should go to the f- To-- in order to get a literal gold medal? No, no, no, no. Okay.

Like, like to get the, the credit for being in the IMO effort is to be in the photo. So it's, it's just a joke. It's just an joke. Yeah. But anyway, okay, could you tell the story of starting this IMO thing? Apparently, it was done in one week. So let me like be a bit more clarify a lot of those things, right? So the IMO effort has been like very long standing.

So Tung and basically, and Kwok has been busy working on this even last year, right? Last year they got-- so I was not back at Google at that time. Yes. So like they had the AlphaGeometry stuff, and then there were like AlphaProves and stuff. So it's, it's a very long extending effort. But I think this year was the-- we wanted to try to like use-- actually use Gemini as an end-to-end model. Basically, no- No second system- No second system- -with AlphaProof... like the text in, text out- Yes...

like a model. And- Even that was a not intuitive thing. I covered the silver result from last year. Yeah. And I was like, "Okay, it's pretty close." Like it's one point off from the silver. Just try a bit harder, you'll get gold. The decision to abandon it, well, I think was pretty bold. I don't know.

I personally was believe, always believe in if you're not-- like in retrospect, it's easy to say this, but it's a bit like if the model can't get to IMO gold, then can we get to AGI? Basically, so it's basically at some point we have to use these models to, to try these Olympic com-competitions. And I think that one of the goals this year was like, okay, we're going to do an end-to-end like text in- Yeah... text out model. That's where like my involvement came in.

So basically, I was not like involved in the IMO effort only until the model training part. So I have to say that, that Tung did, did most of the IMO thing. I just trained the model with a bunch of other- Okay. What, what does that work involve on a surface? So, so basically we, we just prepared the model checkpoint for the actual IMO itself, right?

So there, there's also something that is easily overlooked about the IMO thing was that many times you, you want to chase benchmarks or stuff like that. It's always like thing that you can kind of keep running and running and hill climbing until you get there and then you-- but like the IMO was a live completion.

Like some members of the team were in Australia for the thing, and there was like this happening thing was happening live, it was unfolding live. Oh, it's a very AlphaGo. You receive the thing, you like punch it into your system and then like- Yeah, yeah, yeah. So, so like some of the professors from Tung's team were like in-- when they went to the IMO itself and stuff like that, the conference-- I don't even know whether IMO is a conference, but it feels like there were people there.

[laughs] Like in Australia. [laughs] And then so it was a live thing, and there were people who actually the job was to run inference on, on, on this IMO P one to P six that came out, and they also came out on like different days. So it's like different sets, like one day, one day, two, something like that. So the fun part is that I knew nothing about IMO like at all. I'm not like a kid that took part in IMO.

Like I stood down for that. You're a piano player. And yeah, I played the piano. But, uh, what I only knew was that, okay, we delivered the checkpoint, and that checkpoint was used to do the IMO goal. But then like there was somehow a week in London where everybody gathered there, so everybody was flying to London and then this photo was taken there, and then you get to see how all the different parts like come together and like also being in the other rooms, in the rooms with the other like co-captains and then it felt a little bit like a hackathon thing.

[laughs] So yeah. I think this was like the training process of this IMO model itself was like maybe a week or so. Not the actual, like the whole like basically like- Yeah. Yeah. I think the, the question is I'm still not over the decision to throw away AlphaProof. Okay. Yeah. Basically. I think it's very major.

A-a-and I understand that you have this goal of AGI, obviously, like at some point one model should do it, should do all of it, right? Yeah. But I think if you pointed a gun at me and said in twenty twenty-four, "What do you need is to do IMO and IOI and, and CPC and all the other stuff that you guys did?"

Was you need an LLM reasoning system that knows how to operate a computer and knows how to write Lean and run- Yeah... Lean verifier and all this. But basically, you ROFT the Lean verifier into the chain of thought. Is that the... [laughs] Wait, I- Like, so basically, like, it is not obvious that you can do that- Okay... at all.

Because I think- So, okay, so what, what you mean is that like some, in some way- You need a system one system-... this thing could be encoded in- You need the-... parameters of the model somehow. Yes. Hmm. [laughs] Yeah. I mean, it's just whether at the end of the day you, you just believe in like this like connection is one, one model, lots of parameters. But I mean, there's also two use, right?

There's also two use which, you know, and stuff like that. But I think to some extent the model we-- like I think we should be able to get to a point where to-- like in the past when the LLM first started, the model could not even be a calculator. Now it can somewhat be a calculator. So technically, like a tool like a calculator is somewhat encoded in the parameters of the model. Mm-hmm.

So I think we, we, we'll eventually get a point where whether there's things that cannot be expressed in the parameters of the model is like an open question. We, we don't know where is the limit, but I think we will keep pushing and pushing this limit. So whether like a some-something like- A lean system or like some other things to solve other, like maybe a physics engine or something where could it still-- we still continue to push that, that, that boundary. Yeah.

But I actually don't know, like whether there were a lot of debates about s-symbolic system versus end to- Yes, that's the word I was trying to- I actually don't really know whether there was... Like, to me, I was just like, "Oh, let's train the model." And then when some-some-someone told me to train the model, and then I trained the model.

Basically, there was like overarching, like I-- people at, like the IMO effort that, that decided this. And I also think that because the, basically these specialized systems are very like one-off systems that are like you could create like a chemistry engine, create a math engine, you could create the thing, right?

But at the end of the day, you want one model for everything. So yeah, so I think this kind of fits that direction a little bit more where you have one model for... And then this model was also like launched as Gemini deep thing, as a general purpose Gemini deep thing. So- It's basically unchanged, but with maybe some config toned down a bit. Yeah.

So the, the, the inference time config was like the one served to most people is different. But-- and the full IMO co- like inference config was prese- get, like shipped to some mathe-mathematicians just because of the inference cost, right? But that was good enough to be a general purpose model. I think my take is that this i-i-intuition was what led to the trying to go towards one model instead of... 'cause these specialized systems, there's no end, right? You can create many specialized systems. Yes.

The most I can see in the future is there'll be a model then that, that is, there's something that really cannot be subsumed by a model, then you just use a tool or something like that, right? Yeah. But my prediction is that I think most things can be, can be subsumed by the model. I think, yeah. Let me- AI researchers are quite good at hill climbing.

[laughs] History would say that you're, you have a lot of evidence backing you up. Is this the model output? This is it, right? This is what- Yeah, I think this is the model output. Yeah. And what do you see when you look at this? You see obviously it looks like a well-written prompt. It looks like something a real human mathematician would do.

I-- people did compare yours versus the OpenAI one, where OpenAI is a lot more raw or have to clean up their versions. We don't have to talk about OpenAI, but I just-- I think what is interesting to you when you saw this kind of output? I want to give a little bit like a special disclaimer is that I know nothing about math.

[laughs] Right. So I think the wonderful thing about this era of LLM is that like you can be a, like a AI researcher engineer, and you don't have any domain knowledge, and you can still, yeah, solve a-- get a gold medal in a- It's a universal tool... that you don't know anything about it. I can't parse this at all.

Okay. Like, this is foreign to me. But like maybe a proof is a particular kind of chain of thought. Yeah. But I would say that the other interesting thing that some of the, some of your co-collaborators were talking about, he was just like, "Oh, this is the first example of reasoning in a non-verifiable domain."

Which to me isn't proof by definition verifiable. I just want to give you things to riff on or debates that are-- might be worth digging into. So I think there's go- there's a lot of, aside from proofs, there's a lot of domains that are like non-verifiable, and I think not, not easy to verify. So it's like when people mean non-verifiable, it's like non-trivial to verify or like just not as, not as easy as like the solution of like a math problem because proofs are long form and it's also-- that's why it's non-trivial to, to verify unless you convert it to Lin and then you do all kinds of things, right?

So I think there's a lot of work to be done in like these type non-verifiable domains. Yeah. I'm getting into this territory where I'm not sure what I can say or what I cannot say. Okay. So we, yeah. Sure. That's it. I think another thing that is an open topic of debate was how much domain specific work or post-training was done because you then went on to do the IOY and CPC stuff as well, right? The same model.

I was not directly involved in the ICPC, but I was related to science then. That's all I can say. Yeah. Yeah. Any other interesting call outs, maybe- I'm on- Just on the team. You called out Jonathan as someone who's co-captain on, on this effort, and yeah, basically how does the effort like this come together?

So I think there were four captains for the IMO, two from London, Jonathan was from Mountain View, I was from Singapore. So I think four of us basically trained this model together, and I think one-- I was also trying to receive what Tang was saying. But I think one, one interesting thing was that like we're all in different time zones and we all... and there's something also very interesting about like passing on the job.

There's no like really like fixed workflow how to work together between captains. So it's more like, "Oh, I'm going to board the plane now. I'll be like AFK for twelve hours." So someone takes- So just babysitting the run. Sometimes there are bugs and stuff, the job comes down sometimes. So basically, it's very ad hoc and it's very, it's really between the captains how we decide to like work together. And yeah, but I think it was a kind of interesting time also because we were all flying.

Like, I think the London folks were not having to fly, but I had to fly and Jonathan had to fly. And then like when you visit another country, you have another-- like if you visit an office, you have many meetings. So I was in and out meetings, and it was a pretty, like interesting. And I also think that we-- nobody really knew whether we would get gold at that time because IMO actually has-has-hasn't happened.

Yeah, it, it was interesting and exciting. And then I think like the whole process of this getting verified by the IMO committee and, you know, like, you know, there was like, okay, but we're not going there. But I had to learn a lot about how the IMO works, right? Apparently, the gold score is not even, it's not even a fixed number. It's like a bell curve. Yeah. Right? Yeah.

So it was like a time where you just look at the score, you're like... Like I was even like looking at the-- watching the human partic-participants and then seeing like what the scores were because- Mm... whether Gemini will get gold depends on like how the humans do. So you're like looking at, oh, if a certain percentage, you're like, "What's the-- Do we get gold?" Yeah.

To some extent, you don't have any control over that, so it's- Yeah, but you're just curious, right? Because- Yeah, yeah... that's why I would say that it's definitely more in ter- like exciting. Like there's more adrenaline than like just ru-running on a benchmark and getting a number. It was like a process that, that, that took some time. Yeah, yeah.

But I think overall, if you have specific questions, you can ask also. But I think this whole thing has been a highlight for me. This IMO effort has been- It-- Yeah, I, I would say most people, if you ask them maybe two years ago whether a model could get an IMO gold, they would have said it, like impossible[laughs] Yeah Then the silver helped, right, from last year.

But, like, the fact that you can throw that system completely away and then just take ex-existing Gemini and scale up Deep Think and then just run it for IMO Gold, I think is also, like, very non-consensus compared to last year. Yeah, definitely to some extent, I think researchers were also surprised.

I w- I won't say, like, surprised, but, like, it was more like a pat on, on the back kind of surprise how we actually made a lot of progress in- Mm. We as in collectively, like, the, the-- all the engineers and researchers working on Gemini. There's a lot of progress being made. Just look at how much we went in one year. Yeah. And I also think that- Yeah...

it's just five years ago, right? Not two years, like five year, you just, you just imagine the outcome. Like, you just look at the state of AI now, like just generally the bit... IMO and the ICBC Gold and like also even like things like NanoBanana. If you just look at the AI progress now and five years ago, I think people would think that we already reached like AGI. Some form of AGI.

To some form of AGI. Yeah. We're just moving. If you just traveled, like you take these checkpoints and you traveled back into five years ago, uh, someone should make a drama about this. But I think it's really quite impressive, like, how the field has moved so quickly. Yeah. Yeah, yeah. The hard parts you would say were scaling inference. In what aspect? Like hard in terms of, like in expensive?

Hard as in maybe the most amount of brain power expended on the team. I saw some comments where they were like, "Actually, the hardest part was the inference optimization," or like the very, very long horizon inference that Deep Think needed compared to a normal Gemini, stuff like that. I didn't work on the inference time scaling. Yeah. Yeah, I wouldn't know. Yeah. That is mostly that... Oh, and then there was this, the code name was apparently IMO Cat, which you named after your desk. [laughs] Okay.

That's not really-- Like it was in the-- So I, I think I tweeted about it at poi- at some point, right? Yeah. Like bringing up the tweet. So the IMO Qui- Cat was basically like, okay, it's not like a official code name or something. It's just like the name that the config of the job was, like IMO Cat.

That's, that's, that's the- You just need some kind of name. Y- yeah, I mean, I just like, you know, I just, I like cats and yeah. Yeah, fair enough. That is mostly it on IMO unless you wanna bring up any- anything else. We have other sort of researchy topics, but beyond-- before I go into sort of researchy topics, I did wanna maybe leave the floor to cover what else should people know about the reasoning effort that's going on at GDM.

Let, let me think of where to start- Yeah... with this. What do people need to know? So- You know, the model is very good, yeah, but that's what people need- Maybe an easy one to start with would be a lot of people were focusing on maybe academic benchmarks two years ago. Last year, maybe LM Arena. This year, Pokémon. Pokémon's a very interesting reasoning, visual reasoning, and just general long horizon agent planning- Yeah...

benchmark, and I, I don't know, you seem to focus on it a lot, and I think Gemini did very well, so obviously I think it's something that is easy to talk about. I think I probably should rephrase this. There's actually nothing specifically done for Pokémon. Of course. Yeah, of course. Like there's nothing specifically done for Pokémon. And I think that-- I think Logan had this tweet recently about the recent Gemini III on Pokémon Crystal. Mm. And Pokémon Crystal is like- There's so much more to punch... Pokémon special. Yeah. I think Pokémon is like-- So I used to play a lot of Pokémon, and I'm a big Pokémon fan in general.

And I think it's a, it's a great, like you said, it's a great long ho-horizon benchmark and stuff like that. And I think it's good to check in once in a while on these benchmarks that, like, almost never get contaminated or let people actually, like, don't spend time to- Benchmarks... like huge client benchmarks. It's like kind of silly to like build like, okay, like what are you working on?

If some people are, "Oh, I'm working on AMI. I'm working on HLU. I'm working on Pokémon maxing," or something like that. That's kind of, like, funny. We did interview the Cloud-based Pokémon, uh- Yeah... AI. I think his name is David. And it showed serious flaws in Anthropic's screen understanding vision capabilities. Yeah.

Couldn't-- literally couldn't tell I'm trying to, like, get past this wall, but you keep-- just keep running into it- Oh... 'cause it doesn't know that the wall is there. And so it doesn't have any spatial reasoning at all. I mean, some of it could be like a harness, like the harness thing or like also whether the model has access to, like, game state information or is it completely visual.

Yeah, Cloud's implementation is very game state heavy. They dumped effectively, like, all the, the memory of what's going on in the emulator. Yeah, yeah. I see. Yeah. I think for... But I don't know whether I'm jumping off tangent or something like that. No, it's fine. I, I think solving Pokémon is gonna be more of like how fast you solve it, and then, like, the thing that I have not really seen so far is like whether the model can com-complete the Pokédex. Why is that?

I think you need- Even more challenging. No, completing the Pokédex is so hard, right? You need to plan, you need to like-- you need to search up, like, informa- like there, there's some things if you don't, like, go online, basically you need to like have a little bit of deep research in- Yeah, yeah...

in this. Yeah. There's-- the model will just never know, like, that it needs to trade, okay? If it's able to go online, post on forums, and then find someone to [laughs] like, "Hey, Claude, can I trade with you these Pokémon to evolve?" Some Pokémon needs to be traded to evolve. Yes, or mated. I think it's- Yeah, but- Yeah... yeah, but anyway, I, I don't know. I have not seen the model be able to complete a Pokédex, but completing the Pokédex is really hard actually for models.

So I was-- I think that's actually an interesting, inter-in-- like, like an interesting one. Yeah. Yeah. I wonder what the real world analogy would be once-- Let's, uh, if let's say we, we have a model that is capable of doing that, what can we make it do that we cannot do today? Oh, there's a lot of planning involved, right? Like just real deep research.

Like planning, there's also a lot of planning involved, and it's more-- I think completing Pokémon is p-p-- the Pokémon game, the li-- is very linear, right? The thing, the Pokédex is, uh, involve a lot of, like- Backtracking research... yeah, a lot of research and a lot of... Yeah. So it's a probably a different nature- Yeah... itself.

Is that as interesting to you as, for example, a lot of other people in the AI for science world are trying to discover things that you cannot look up, right? Novel knowledge. Yeah, novel knowledge. Because basically what you're saying is we're not even there yet. We're at the place where models cannot consistently apply knowledge that they look up, right?

Like you, you give Gemini access to a web search, and you say, "Okay, go and try to collect all the Pokémon in a Pokédex." You don't have high confidence that it'll do it. I, I don't know if someone's actually tried. Probably not, right? No, I, I think the hard part is actually like- Trying to use the-- like synthesize the web knowledge and then apply it in, in, in the game itself with all that visual state going on and stuff like that.

It probably will be solved in one or two- Yeah, yeah. No, it's not- It is challenging- It's not challenging, but not like super- But like is it interesting? Like you're basically just-- the, the task really is can you look up the guide to do it, and then can you apply the guide? That's it. You know what's even more s- intelligent than that? Creating the guide.

Like being the first to figure out how to create the guide, [laughs] which is what in- Oh, yeah, yeah. But then when it comes to this one, it's mostly there's a exhaustive search thing. The model will just try and try like humans guide for that. Yeah, yeah. Okay. So that's actually less interesting to you. Interesting. Okay.

Actually, to be-- okay, when you think about it, it's like not super, super interesting, but it's okay. It's just that I have not seen a model to try to do this, so it's- Yeah... that's why. Uh, yeah, I think like efficient search of novel idea space is interesting. Obviously, you can brute force anything, but we're not talking about brute forcing. We're talking about trying to create an AI scientist.

But novel knowledge is actually an interesting thing that, that I think is gonna be quite a big thing being able to generate- Yeah... novel. Google has done stuff there which I don't-- you're probably not that close to those teams that has done AI scientist work. There's some things that have been-- it's like, for example, if you freeze the model weights at like twenty fifteen, you freeze time at twenty fifteen. Mm.

And then you-- even with the current model, let's say you have-- okay, let's assume there's no l-l-leaking of information somehow. Can-- if you ask the model what's the best ML, okay, not twenty fifteen, like twenty twelve or something. It will just tell you that SVMs are like the best, right? This is the way that machine learning works in general, right? Then the question is can you invent the transformer, right?

It might not be able to-- like even today's models, they b- might not even be able to invent the transformer. Like if you freeze the time at a certain time, and even you bring the te-- I mean, the model is a transformer, so I just say there's no-- assuming there's no leakage, there's like, like- Oh, yeah.

Yeah, no, totally possible. Yeah. Right. So, so I think there's still a lot of open questions about like whether the model can really, you know, innovate and generate like, ooh, really novel- Yeah... uh, uh, knowledge. Yeah. Yeah. One related question on that, which I think is related to the-- to the Denny paper, which is I think people have this sort of mythicism on what reasoning is. Mm.

And reasoning, if you really demystify it a lot, it's whatever happens inside the chain of thought tags- Mm... right? And you post-- you, you-- you're eliciting that reasoning behavior from some stuff that is already latent inside of the pre-trained corpus. Is that-- that's one version of this interpretation.

I think these days, like reasoning itself is very vague, and it's very open. So it's like most-mostly different people will have different definitions of what reasoning is, right? So I agree that like this chain of thought is like basically when people think of reasoning, they, they associate it with chain of thought, and obviously it's what happens in, in, in the thinking and the, okay, reasoning, right?

But I think these days it's more like I said in the earlier part, it's like reasoning in RL is almost like the s- like basically it's anything that is like post-training to elicit capabilities, basically. It's like, like RL and post-training to elicit capability, capabilities. So I think the actual like technical definition of reasoning is making models better with thinking and post-training. Okay? Yeah.

So basically like RL-ing the model to think better, right? And thinking is more like thinking tracers and tr- thought trajectories and stuff like that. Right. There's also this line of work for la- like latent thinking and, and stuff like that. Like whether latent thinking and discrete token thinking is like going to be the same thing or like something like that.

It's like a open question. Meaning adding extra tokens to your vocab that represent or- There's all this-- I forgot what's the name, like these academic papers that do these loopy things or- Track tokens. Or like they basically, instead of decoding discrete tokens, you actually simulate this by doing this in latent space, right? Uh-huh. So when you do chain of thought thinking and like re-reasoning, you basically decode extra tokens, hide it in the thinking tag, and then you do decode stuff. But like latent thinking is basically you, you just don't decode- Yeah...

tokens. You just- Don't bother... by- It might start speaking-- the native language of thinking is numbers, not passing it through some filter of English. And sometimes it might start thinking Chinese or something else. Yeah, yeah. Generally, I'm not a-- I'm not really-- I don't really believe that model thoughts have to be the same with human thoughts. I'm actually like- Yeah...

generally in ML, I'm more of the school of thought of let the model do whatever it wants in general. There was a discussion. There, there's a latent representation hypothesis paper that I think you are maybe sympathetic to if you haven't already read it. To me, sounds obvious. Basically, image models will have the same idea of what a laptop is versus a text model will have-- they converge on like the same latent- Yeah...

and obviously you can align them and you can do all those stuff with them. And so it, it totally makes sense that their concept would be just a vector of numbers that represents laptop. That's the concept. Yeah, yeah. And okay, maybe you have some numerical differences between one model's idea of what a laptop is versus another, but it mostly would be the same. Yeah, very interesting.

The question I was kinda leading into was that because there's-- now we're in this age where LLM text is in the corpus of like stuff that we train on, where it's a little bit of a recursive loop, right? Like the reasoning tokens are out there now. And so pre-trained models themselves, pre-pre-train based models- Yeah...

are also capable of reasoning, and they're increasingly so as more and more reasoning text goes into the corpus. Isn't that interesting or is that worrying? [laughs] Do you actually see much reasoning trace on the internet? So- I've never seen those though. I would say that- Like on Hugging Face? Yeah. People are publishing that specifically.

As, as to whether or not people are-- researchers are actually including that in their training corpuses, who knows, right? Like this, that's their, their choice. But I would say that percentage on Common Crawl that has CoT tokens in there went from zero to zero point zero zero one percent. Mm. And it will just go up over time because like people are publishing it.

Yeah, but I think if the sources are like quite clear, you can actually like filter away those because usually people put it on GitHub or put it on- Do you want to filter it? Maybe you don't. That's, that is a choice for the, for the- Yeah, quite re- quite literally the whole reason why-- I don't think we covered this in our previous part, but- Two years ago, a lot of people were like, "Oh, you just include more coding tokens in your pre-trained corpus."

It will be- Wait, but the coding tokens are different from like coding token. No, it generates this outside of code for reasoning. Oh, that was like- You don't believe that? No, no, no, as in like, I don't know if it's still true today, but I see. Yeah. That, that was just our general coverage of reasoning. I would say that there's a, a lot of interesting work here and more to do.

Maybe I'll cover one thing which I know that you have personal inputs on, which is that you have started using AI coding. Oh, yeah. So I actually don't really u- use much AI coding in the past, but I think we've reached a point where AI coding has started to become really useful. Like, okay, so before AI coding, the most-- the, the thing that I find the most useful about, like, these models in general is like when I have these big spreadsheets of a lot of results and I just need plots of it.

I think models can quite good at just screenshotting a plot of this. I hate making this method like stuff about. It's so annoying. Okay, but that's basically, like, one thing that I can remember about, like, how I used AI in the past. But I think AI coding has started to become to the point where I run a job, I get a bug. I almost don't look at the bug.

I paste it into, like, Antigravity and, like, I tell it that it would fix the bug for me, and then I relaunch the job at that like... Beyond, like, vibe coding, it's more like vibe- Training... v-v-vibe ML or something like that. ML. [laughs] I would say it, it does pretty well most of the time, and it's actually there's, there are classes of problems that it's just generally I know this is actually really good for and in fact may probably better than-- like, I would have to spend 20 minutes to find, like figure out the issue and then- Okay...

fix the thing and then relaunch. To be-- so yeah, that's very interesting because I would say like level one vibe coding is you actually know what to do, you're just too lazy. Yeah. It's just, "Ah, just do it for me." Like, "I've done this a thousand times." Like, "Just go fix it." Like, "I know exactly what to do." Here you're saying it's like on, like, the next level where you actually don't even know.

It's, it's investigating it for you. This one is like the answer looks right, you would just ship it. At the start I was a bit like I did check it and look at the thing, and then at some point I'm like, okay, maybe the model knows better than me, so I'm just going to let it do its stuff, and then I will re- relaunch the job based on the fix that the model gave me.

And I think the models will just keep, keep getting better and better. Yeah. So yeah, it's something that, that- Yeah. But I also think that I mean recently there's this, there's Antigravity. I think, I think also because these tools were not like that. In Google infrastructure it's not that easy to...

Y-you don't-- I'm not that familiar with what is available outside, and when I was at the startup, I didn't really-- I think the models were not, like, s-so good like one and a half years ago. So it's also like a, a forcing function that, like, it's also people are like, "Oh, try Antigravity, it's a game changer and stuff." Like, okay, so I just started using and yeah. Yeah.

You spent some time with Varun recently. What did, what did- Oh, no, I really just say hi and greeted. Okay. I guess you were telling me you're an AI researcher that doesn't even use much AI, and like now you're actually like AI pilled [laughs] as a user. There were so many moments this year where AI suddenly crossed that, like, the immersion thing. So I think AI coding is one of them that we just discussed.

I think, like, Nano Banana also got to the point where I usually, like, we make these images, it's just like a little for fun and just thr-throw your friend or something like that. But like Nano Banana actually really got so good that- You can use it for charts... that, that- Yeah... that, that you can use it for like basically, yeah.

So it's getting really good and I think, yeah, th-this year the stuff like... and even things like the past, many of these LMs will like halluc- hallucinate things a lot, but now I just trust it like automatically. I think we just-- I think p-people are just enjoying the utility brought by- Yeah... by, by, by these models. So now I'm like... I was always AI pilled. Like, AI is a good thing.

Like, I, I don't see how anybody can disagree with that, but- Yeah, I know, but you are actually using it for things that you are high expertise in, which is your own ML work. Yeah, yeah, yeah. And just to, just to confirm, you, you, you-- do you have a special version of Gemini that you use internally that we don't have access to or you're-- it's like public Gemini?

Or do you have- I, I, I think it's the public Gemini. It's Gemini. Okay. Yeah. The, the-- I'm just saying, like, it would be entirely reasonable to train Gemini internal for only your code base and your work. Oh, actually, I'm not sure, though. You see, these things are just like really- Yeah. It's-... like strategy- Written away for you... strategy written for me. Yeah. Yeah.

But obviously, if it obviously improves, improves your pro-productivity by, I don't know, ten percent- Yeah... worth it, right? Yeah, yeah. So I, I think that's interesting. A-and there's the interesting thing, le-levels of how much do you trust it, how much of your jobs do you automate away- Yeah... that you no longer need.

There's also the question of, I guess, about how people come up and train in the field if they-- if you no longer need juniors, because the Gemini is your junior ML, ML researcher. So I think these are, these are all interesting questions. I want to say one quick thing first, right? So I think that when it comes to, like, whether a model can be like a junior SWE or like something like that, right?

So I think if you think of it this way, of if a job from a one, 1x SWE, one time SWE can be replaced by a model itself. But let's say you are a manager, right? The objective that-- the metric you track is, like, your time, and then if you can have a model that saves you, like, the same amount of time as the work that your reports do, but you don't actually, like, replace one person per se, but you- Yeah.

A little bit from everybody. Yeah, right. Then you can-- I, I definitely agree that, okay, like when you count the net time saved, there are times where the model can fix bugs that, like, would have cost me like one day, right? One day is huge. Right. And these things are definitely like if you-- I don't know whether anybody has done any like real, like, metric evaluation on these type of things, but if you use time as a me-real metric and then not as a number of, okay, maybe three hours, like, kind of me-me-metric, right?

But these things are not like, like, going to replace one person as, as it is, but more like a passive aura that buffs everybody. The in-game terms, right? Yeah. Yeah. I often think of myself as a bard 'cause I tell stories and I, I plus everybody around me, you know? Like, like that's, that's a, that's an ideal situation for me in a D&D group. Okay. Yeah.

No, I don't play D&D, but okay. Yeah, I, I get it. I agree. Okay. That's it. They're the kings of Phantasmora. Support, support hero. Support, support. Yes. Yeah, okay. AI support I think is very encouraging. I think, like, where is it still not working for you? That you've tried and you're like, "Oh man, I expected it to be better." Oh, there are times when models get-- try to get lazy and try to fix something in a-- like, they are still- We will have- They get lazy, and then they try to, like, gaslight me into thinking that, like, the bug is fixed. Ah.

So there, there are still classes of-- there are classes of problems that are, like, very easy for the model and very hard for humans. There are some things that are very easy for humans, very hard for model, whatever paradox and stuff. So it's still very hard to characterize these, these things into these proper quadrants and stuff like that.

So I would say that the capabilities on models these days are good enough to be r-like, really helpful, but, like, it's still-- you-- it's a bit like it still has some... But yeah, but I think this will-- like this-- I don't think there's anything that to, to be done to specifically, like, focus, fire these things. It's more like general capability improvements.

The models just get better over time, and then these things will just, like, go away. You say that-- okay, so yes, I think obviously in the grand scheme of things, just trust the process, keep scaling in every dimension, and, uh, things will just fall away, things will emerge. But you've also said in the past, I can't remember the exact tweet, where you were like, "Each additional data set compounds over time." They're just small additions.

And I would say that when you s-say things like focus, fire on things that you would think humans-- it's easy for humans, hard for machines, those are easy wins where you can just add a data set that would focus, fire on that. Mm. And isn't hill climbing just a sequence of doing that until you reach AGI? Okay, so I, I get your point.

I think that it's true that sometimes a lot of progress on the whole is just a series of small incremental changes that, yeah, that push. And th-that, I think that's accurate. That's true. There's also-- it also feels that there's also a lot of, like, small mi-- like, seemingly m-m-minor, for the lack of a better word, like, that push AI to the state where it is today. So I definitely agree.

So nothing aga-- like, against people who are like focus, fire, but it's just that when I mean that, like, it might be not easy to focus, fire on things that are, like, not very easy to characterize. So it's just that, like, when it has-- there's something targeted, right? You know, like, okay, I want to improve this capability, add some data.

So I think, like, defining, like, the evaluations, defining the problems and stuff like that is, like, characterizing it. And if it can be characterized, and then, okay, then fine. But I think it's like what I was trying to say with the coding is that these things are not even some of the class of problems are like...

I don't work on coding, but, like, people who maybe work on coding, they know, like, they have, like, terminology for different types of failures, but so maybe somewhere somebody is focusing fire on this and then make the model better. That's great for everybody. [laughs] Yeah. I mean, that's why it takes 1,000 people to, like, get all these things together. AI is definitely, like, a big collective- Yeah... effort these days. It's a big machine. Yeah.

It's, it's really crazy. Okay. So I just wanted to broad-broaden out to general things people are talking about in the community on research. Yeah. Which again, I know that you are very locked in, so you don't necessarily have read all the papers or anything, but we can just riff on ideas. Okay. Yeah, yeah. You can obviously ask me what I think as well. Yeah.

Is attention all you need? So attention and transformer has been-- is, like, a core idea in the recent times. Like, pre-training and scale is the thing that made attention and transformers, like, actually shine, right? Because without-- I think the first transformer paper was this, like, a machine translation thing, and then basically, like, GPT and BERT were the ones that, like, actually showed the full, like, big potential of this idea.

So in terms of, is attention like really, really all we need? Like, uh, probably no. But I think it's, like, one of the-- from the architectural point of view, also may-maybe no. But it's not all we need. It's-- but we, we need it definitely. I, I- So what else are you thinking about on that same level? Are you talking about MOE stuff? What do you mean when you say it's not all you need?

Like- No, you definitely need, like, you definitely need, like, the scale of pre-training you need- Okay... or the tokens you need, like RL. I think when I say-- when people say-- I say it's attention all you need, it's, it's mostly- From architectural point of view? Well, will transformers get us all the way to AGI, right?

I guess it's the- Oh, so, so basically when you get to AGI, it's the problem- Will it still be-- will it still be a known architecture or, like, meaningfully different? It will be a transformer, I think. Really? Like, like you-- like people-- it depends on what you call it, but I think unless the paradigm shifts completely, which is-- I mean, as a scientist, you cannot, like, like, completely say no to like-- like, that this would never happen. But my feeling is that it's been, like, what? Like, almost ten years since transformer. Twenty seventeen. Nine. Twenty seventeen.

Nine years, like, since, since the transformer. I think we have not replaced self-attention, like, it's in some form of it. Like, you could rename it, you could name it something else. You could sometimes- You can do local, global style window. Yeah, it's still a transformer in the end. And I think, like, that's not going, like, anywhere unless the whole thing with, like, backprop, like, everything, like, goes-- like, the whole thing just changes completely. Like, then there's a different story.

That's a different conversation to have. But if it's still within the same scope and bounds. So I spent a lot of time thinking of-- about architectures and, like, whether there's alternate architectures and stuff like that. Yeah. Okay, at the sequence processing level, like there's- There is the ultimate- Yeah, it's-... sequence-to-sequence transformer...

it's probably the self-attention is there was this whole big era, which I was also involved in this era, where people tried to, like, undermine the attention as much as possible. Like, they tried to remove it, simplify it, make it efficient, like this whole, like, like, efficient attention era. At the end of the day, the outcome was always like, "Oh, remove all the attention, but we have one layer of self-attention there, and it still works." Like, that's at the end, like, of the, like, always the story.

Which even Noam at Character, he published some stuff about how he has some ratio of mixing of local and global attention, right? Like, basically still attention, but modifying it quite a lot for- I, I would consider local and global attention to be like still, still attention. [laughs] Just like how much you're skipping it. Yeah, yeah.

The only, the only question is that if the formulation changes too much, your QKV becomes like A, B, C, D, E, F, G or something like that or some- Okay. Maybe I'll give you some motivating constraints- Yeah... in order to do this. You guys are still charging two X for over 180K token context or two 40, something like that. And the max is, theoretical max is two million tokens, right? What if we need two hundred million?

Is there some point at which where even this concept of Input token context is irrelevant because you are doing continual learning, uh, th-that kind of stuff, where you're modeling it as, okay, the AGI will be achieved through a sequence to sequence transformation, therefore an attention is the best sequence to sequence model or architecture, therefore attention is all you need.

But I think other people are like, sequence to sequence doesn't accurately capture intelligence. Well, but that's not really about sequence to sequence. That's more about like the whole b- gradient design and backprop thing, right? Mm-hmm. It's not architecture itself. That's, that is a problem that we-- that is more of like the learning paradigm itself rather than the architecture itself.

I think the architecture is just basically like the interface between the learning algorithm and the tokens. I think it's more about the learning algorithm itself, and this cont-- like continual learning this like there's many ways to think about processing many like insanely large like context, right?

Like two hundred million, one billion tokens or something like that, right? Like whether it's gonna be like you have a new le-learning algorithm that every time you run inference, you, you learn on it, right? Then you can technically have some kind of memory like human being is learning as I'm talking to you, right? So that, that's also like one way.

The other way is like whether, okay, maybe somebody will say that, "Okay, the attention is like just too expensive for two hundred million, one billion context, so we need new architecture." Or some people will say that, "Oh, we just improve the chips like accelerators." So I think many ways to interpret it, but I think it's like if it's about-- there, there's a lot of fundamental things that if it's about continual learning and stuff, there's a lot of fundamental things about the learning algorithm and, and stuff like that as well.

Yeah. That will have to change? I think like the learning paradigm and architecture and stuff like that goes like hand in hand. And I think as the field progress and ideas just stack on top one another, right? So there's also this thing about a idea that was proposed has to be compatible with all the work that have been done before to, to shine, right? It's a bit of variant of this hardware lottery like by, by Sarah.

It's not the hardware lottery that I wrote about the TP-TPUs failing, but it's the, it's the original hardware lottery, but it's more like a bit of lottery of like the things proposed have to play well with the things that were proposed before. So it's a bit like going down this local optic minima to some extent. So now we are like in this local minima of like transformers everything, everything, right?

Maybe it's n-not easy to like get totally out of this because al-also a lot of people's investment- Optimization... optimizer have been, have been done. So the things that play well need to play well with the ideas before. And the, the way I see it now is it's very difficult to like- Yeah... c-come out of it. Okay, I'm not entirely convinced.

I see what you're saying, but let's call it Gen AI. I fucking hate that term. It's still a very young field, and so yes, there's been eight years of work on the transformer, but what's that in the grand scheme of things? Maybe we're in a local minima and we've gotta nudge ourselves out of it. [chuckles] I do wanna leave that open-ended. I don't have an idea.

I do, I do think that people are in, let's call it what Ilya Sutskever has been calling the age of research, right? Like we're- Mm-hmm... we're like, okay, we scaled up what we can scale up. There's-- we know what the next maybe one, two orders of magnitude look like in scaling on every dimension that we know about. But what is the next dimension to scale? There's this like m-mis, misunderstanding a little bit about like, oh, the last five years has just been like scaling things, scale. So- Okay, please tell me more. Right. Yes.

I, I think- You, you made that joke about sc-- now we scale res-researcher salaries. Okay, let's not go there. [laughs] But, but I think that ideas like matter, and I think that there have been a lot of good ideas in the last five years. It's just that maybe it's just not-- so it's not been like blindly-- like if you took a MLP, right?

Just like without a self-attention and you just, "Okay, I'm going to throw like hundred trillion dollars on this and scale up that thing," and the thing- It's never gonna work... it's never gonna work. Yeah. Yeah. Yeah. So there's no-- like there's part of it there's also like I think the bitter lesson gets used too much in like too conveniently used around.

But actually, there's, there's also a little bit of a not a bit, there's also a sweet lesson where it's like ideas matter. And I think even to-till today, right, like people downplay ideas and stuff like that. Yeah. Do you think the rate of new i-- without being specific about what ideas, because obviously you can't share, but like do you think the rate of ideas has increased or decreased because there's like kind of a law of diminishing returns?

Are they smaller? I think the number of ideas is always proportional to the number of researchers working on a certain problem. [chuckles] So by definition, by definition it should increase. But I think the number of ideas that actually work is not decreasing compared to the last-- like it's-- we are not in the era of diminishing returns yet.

Well- So I think ideas are still very important, and there's still very good ideas that are game changers that are being invented. Yeah. Yeah, yeah. And I think I know the answer to this, but is the closed lab advantage increasing versus open source or decreasing? [chuckles] Like the Chinese labs, they say they keep publishing open source models and some of the American labs as well publish open source models.

Would you say that the ideas that I see there, NVIDIA has Nemotron, OpenAI has GPT-oSS, these are all basically checkpoints on what is publicly known about training models as of this year. You know- Oh, okay, okay... they are like it's declassified information 'cause everyone- Yeah. Like- Okay. Yeah. Everyone does this. I think that the gap is increasing.

I don't think it's, it's completely predictable from stuff that you said before [chuckles] but- I think the gap is definitely increasing. Yeah. I think that would make-- that justifies researchers. Otherwise, what's the point of having researchers [chuckles] if not finding new tricks that compound over time? Yeah. Um- Yeah. Yeah. But, but definitely I think it's, it's, uh, it's increasing. Yeah. Okay.

I'll do a side tangent. I don't know if you have any comments on this. Okay. So then this is very related to NVIDIA's recent purchase of Groq, which I don't know if you have views because you're, you're very TPU centric, but are we memory or compute bound? And this is re-re-relevant to the transformers discussion of like- In terms of what, like serving? E-e-exactly.

I think the, the classic view is that we are compute bound 'cause we just need more compute for pre-train and RL and then inference. Yeah. But actually the- Counterargument I would make a-against this is I actually have these charts of Moore's laws. I wish I could just pull it up easily. Moore's laws of the scaling of compute versus scaling of memory versus scaling of network and bandwidth, and compute has a much higher slope of scaling than the other two. Memory, what, the cheap memory? Yeah. Like, honestly, I don't think about this, this memory bound that much, so maybe it doesn't... Yeah, so I would disagree- Yeah...

with it, but I don't have high confidence in, in- And because you're mostly on the research side, less on the inference side. Yeah. There's, yeah- Maybe the inference guys would be like, "Oh." Yeah, yeah, yeah. I don't think about... I don't wake up and think about serving, so yeah, maybe I don't think about the inference band- Yeah... that much. Yeah.

My, my previous line of discussion here was like, NVIDIA was very fo-foresighted buying Mellanox because it, it actually is the real bottleneck in scaling- Mm-hmm... uh, 'cause it has the lowest Moore's law, and then the second one now is memory- Mm-hmm... which is very interesting. [laughs] Okay, okay.

But honestly, I don't think about, uh, doesn't think about this, this, that, that, that much. Yeah, yeah. Understand. Okay. Data efficiency. So this is a joke, but implicit in this is that there's some kind of maximum data exposure, right? And, and so I-- so previously I would say that a lot of the training paradigms is like one epoch is all you need.

That's the- Yeah, yeah... mini title of this idea. I would say that the real number maybe is between three to four epochs, and I do wonder what the theoretical limit of data efficiency of a model in terms of training and compression should be. I, I don't know if that means- Data efficiency and-- but basically but you're asking the question in a way of asking, like, how much repeats is tolerable. Is that- One is we've-- in, in-- tolerable is contingent on does it actually improve in meaningful [laughs] ways. Oh. It's not about, like, you actually want to do it for its, for its own sake.

But I do think there's that, and then there's also just the sheer amount of stuff that we can learn with limited data. So you say, as they say, like, we are-- you are not compute bound, you're not memory bound, but let's say you are data bound, right? Last time that we were on the podcast, we talked about Chinchilla versus inference optimal training. But now actually I think a lot of people are even talking about, like, data optimal training.

Like, given limited data set, how well can you learn from it? I think that's an interesting research direction that not enough people are talking about. Maybe it is something that is commonplace in the labs, but it seems very clear that we are very unoptimized with regards to how much we, we learn from our data. I'll just put it there.

I think in general, the, like, learning m-more, like extracting more from every data point is definitely valuable, but I think that is also related to the fact that we're, like, running out of tokens in the world. So I don't work on data for pre-training, and I think things that I say would- General state of industry, not nothing- The general state of industry, right.

So I think that the-- I don't even know whether data has, like, diverged, like the way that these things are done, it has diverged too much across these labs and- No... open- There's a lot of cross, cross-pollination for sure. Cross-pollination? Okay, okay. Yeah, but I don't think about data that much, like the pre-training data that much this. Yeah. Maybe earlier this, the first half of this year- Yeah... I would have said that kind of pre-training is dead, and that everyone's, like, just funneling all their work towards RL.

And you-- we had this, like, grok chart, which was very interesting, where we're sending the same amount of compute on- It's like grok... pre-train as to-- You think it's a psyop. No, I don't know why. [laughs] I have no idea. Yeah. I think that people are taking it seriously. They are like, yeah, okay, whatever. E-especially in the agent labs like Cognition and Course, they're taking the open source models from, from whoever and then adding, let's call it pre-train scale RL on top of it- Mm-hmm... if they have that level of info which, uh, the data which they do. Mm-hmm. Which is very interesting.

I would say, yeah, this data efficiency argument, yeah, I-I think to me is also more trying to discover new paradigms of learning in order to get where we wanna all go, which is, yeah. Mm-hmm. And the existence proof is humans, right? Your two-year-old daughter can-- is much more capable than, than an LLM in some things, having seen way, like, eight orders of magnitude less data. That's very interesting.

[laughs] Yeah, comparing human learning to machine learning is definitely like, like- Purely as an ex-existence proof that we could probably do better. Three examples of dog. Yeah. Fourth example of unidentified animal, I can probably tell it's a dog as a human. But machines, classically, you take twenty. The data efficiency of humans is definitely way higher than, than, than models.

Yeah, the only question is that where does this thing come from? Is it actually like putting more flops on every token or like maybe it's like back to the question about whether the transformer is the optimal architecture. Maybe it's backprop, it's the problem- Yeah... maybe it's the off-policiness, maybe it's the- Yeah.

So what is the-- like, where is the bug, right? [laughs] Where is the bug, right? Exactly. Exactly. Uh, but maybe it's a feature, not a bug, I don't know. But, uh- So now, okay, we've identified probably, it took me a while to get this across. This is the kind of data efficiency I'm, I'm talking about. I think it's emerging.

Basically at the end of every year, I like try to take bets as to, okay, what will be the big themes for next year? Yeah. I think this is one of them that people are really trying to focus on. Be-because you're feeling this data crunch, even though everyone's like still investing in data. I forgot to mention that I would say that I've been wrong on pre-training being dead.

Yes, I've now met pre-train leads from both Anthropic and OpenAI, and I've also seen the, the talk from the DeepMind guy recently, and so everyone's investing in pre-train still, which is like nice to see. Nobody said pre-training was dead. I know. [laughs] No, it's a, it's a theory that we're trying to disprove- Okay, okay... or, or, or prove. Yeah, yeah.

Anyway, so I think, okay, let me wind back to my general idea, right? So yeah, data efficiency seems worthwhile. You would treat it as like, "Okay, well, show me where the bug is and I'll go fix it." Yeah. We don't know where the bug is. Yeah, yeah. We just have existence proof that it could be better.

And then I think the final logical chain in this for me is that everyone is focusing on some idea of world model As a version of this form of more efficient learning, which potentially might not take the form of a sequence to sequence transformer. I don't know how that works. Like, definitely a little bit out of my depth here.

To me, that is more efficient because every world must be internally consistent, and if the next piece of evidence come in and invalidates those worlds, then you no longer need to pursue those paths e-ever, and you can just narrow in on the world that you've identified. And so to me, that is learning, where you're learning to fit world models. Or to the actual data. Yes. Oh, so yes. Mm.

Maybe you can treat the learning process as curve fitting. Yeah, so you're learning the world instead of learning the-- so the world model, yeah, so or learning the world model, right. Okay. By sampling multiple world models and then finding out which one fits the data the best. So I guess my query, is this what people talk about?

Is this-- if you-- I mean, obviously feel free to attack it because I'm just spitballing it. But this is what I pick up from talking with multiple people about, okay, what are you talking about with wor-world models? What are you talking about data efficiency and learning efficiency, and like how do you gel it all together in a cohesive sense of the future where we can actually- Can, can, can we def-- what's the definition of wor-world model at the start, from start?

Yeah. There are two kinds. Okay, okay, go on. Yeah. First kind is the VO kind. VO kind. Or the, what's the other one, Genie? Yeah. That, that DeepMind has, which is the sort of video world model. Yeah. You model everything with some kind of Gaussian splats or whatever, and you're like, you inhabit those, that 3D space. Yeah. Second, let's call it the Yann LeCun/Meta school of thought, which I don't know if you're that familiar with it.

He has published the JePa architecture, and then separately, FAIR has also published the code world models, where you're basically, specifically for code, very interesting, you are executing code and modeling the internal state of the execution environment as you go line by line. Oh, okay. So the, the LLM actually like learns to predict th-those things. Yeah. And actually it seems a lot more efficient at the scale that they've tested it out, which is very cool.

Which definition are, are you anchoring on? The third one. The code one, the code world model? That's the second one. Oh, second one, okay. Those two are bundled together. Yeah, yeah. The JePa fans are probably hating me right now because I'm lumping all Meta's work under one school of thought. Yeah. But whatever. Okay. Okay.

The, so the first one is VO, Genie, I feel is like super spatial intelligence, those kind of video based world models. Second one is some execution or some sort of explicit modeling in the, uh, as you, as you sort of run through the, the corpus. Yeah. And then I think the, the third one is this amorphous thing which I think people are trying to get to, where they are doing what I said about the resolution of possible worlds and you're curve fitting as you learn, as you inference.

Yeah, but what is the world model itself, itself? Is it like- It is a mental model of where everything is and how you think the world works, how, what I think you think, like everything. Okay, but the technically it's like a- It is something in the latent space. Okay, okay. So you can, for simplicity it could be just be like a transformer model between and- Yes... and, and so- Yes.

So to, to me, that is the most coherent thing to the current paradigm, which is you cannot actually do this in current transformers. I think the way that you train it will probably have to be different. Okay. I see, I see. I, I don't have any conclusion here. I'm just throwing it out as something where I know you're interested in this kind of stuff, and I don't have that many knowledgeable people to talk to about it.

[laughs] No, I don't think about world models that, that often. I think because world models are just not really well defined in the first place. Yeah. But I think when- So don't necessarily have world models, but the problems is- Data efficiency... learning efficiency and maybe, I guess, accuracy or like ca- AGI capability that is not easily unlocked right now on our current path of scaling. Yeah, yeah.

So I, I think, I think when it comes to like, like data efficiency, I think it's more like a unbelievable of finding ways to spend more flops per the token, right? Because you actually, basically you're-- if you are data bound, you want higher data efficiency because you can learn more from every data point. You squeeze, you squeeze out more points, right?

So things like that can extract more, can use more flops on every token is definitely like a form of data efficiency. Then there's the learning algorithm, right? Because I think there's this-- there's a different scaling law for like humans is this, machine is this, uh, like dogs are this, cats are this. There's this different like exponent, right? There's the Ilya chart. Right.

Yeah, yeah. There's this famous, famous, famous chart. Uh, and point one and point two are just like not entirely like d-different things because it could be that better architecture is actually just spending more flops per token. So if you are-- you come to a point where you are very data bound but not compute bound at all, you just find algorithms that spend a lot of compute on every token, on every token. So I think the, the overarching point is just that, okay, that, that, that it's a learning algorithm thing for data efficiency and then if whether the correct way is actually just to apply more flops per token just to squeeze out more from every, every data, every data point. Also because humans actually don't like-- like they are exposed less.

When you say less or more data, it's also very ambiguous because they are technically like on like twenty-twenty-four slash seven, and then you have- And it's mostly visual. Yeah... have a lot of like different types of inputs, right? And whether they actually spend more flops on everything they listen is also a question because maybe they are just data efficient just because-- I mean, I, somebody needs to count like how much flops the brain use to process, like how much-- maybe they're just spending more compute on every token, and also maybe the learning algorithm is different, so.

But I agree that data efficiency is very important given that, that I think we're gonna like there's limited amount of data in the world. One more thing before we go into DSI. You know how like we're talking about RL and like- Mm... yes, you're working on RL stuff. Why are people paying so much for RL environments? Wait, so who is paying for RL environments? OpenAI and Anthropic at least.

I d- nobody said anything about DeepMind. So a lot of the model labs that are not you are well known for paying at least seven figures for external startups to create RL environments for them to train in. Okay. And I think the question is, if your, your models are so good at coding, why don't you do it yourself?

And so I think there's some amount of expertise that's being distilled from human experts into an RL environment that you can then let your agents run wild in. But I'm curious if there's any other deeper insight than that because I'm not satisfied with my own explanation. RL environments that are like, they have a lot of domain expertise are probably very valuable.

And actually I, I don't know specifically about what RL environments people are actually explicitly buying. But what, what's the thing that you're not satisfied by? Like the- It was just so valuable and a lot of people are saying like, "Look, it's a Next.js app inside of a Docker container that logs stuff out when you send inputs in."

Then you could probably do it yourself internally, right? Why you pay so much for some startup that you don't know to do it for you? I, actually I have no clue about what- Yeah... like why this is happening. Yeah. Yeah. I have no clue. And, and a classic example would be like, if you wanna build computer use agents for buying things on a, in e-commerce, you would want RL environments that perfectly replicate maybe the top 1,000 e-commerce websites.

Yeah. And then you just parallel roll out on all of them. Does that seem valuable? [laughs] I don't know. All right, cool. DSI and LLM REXes. A big bet for me this year for my conference was we actually like started focusing on LLM REXes. The other- Actually, what's the motivation behind starting LLM REXes? Track? Yeah.

I think REXes is the king AI problem in consumer, is the single most valuable thing. All your feeds, any s- even search is REXes. Yeah. Basically it's search basically. Like it's retrieval. It's the God problem, right? 'Cause REXes is ranking, but then also filtering, also personalization- Yeah... also [laughs] re-indexing and- Yeah... and, and like performance. It is the God problem and you get paid a lot for it.

Engineers are not that excited by it, which is very weird because they don't see... A lot of them don't work on REXes, and they probably never will. Yeah. But they don't see the monetary value that can come out of a good REXes. The other two pieces of updates for me, which I actually didn't even know that DSI like directly tied into this- Okay...

was, one, Twitter publicly adopted their feed algorithm as an LLM REXes. LLMs are just used everywhere now. But whether it's actually like a gen- Like a big LLM. Like whether it's like a generative retrieval type of- Yes... models, like it's like another question. Correct. I- is it? We don't know. All we know is that they have said that they have swapped out their current REXes for an LLM-based REXes. That's all they found. Okay. Okay.

But what has, what is published is YouTube, where they actually adopted semantic IDs for YouTube's REXes. Yeah. And YouTube is obviously a big deal. Is it like public information? Yes. Okay. Okay. They came and did a talk about it with us. Oh, okay. And then they published a V2 this year as well. Okay. I see. It's more info.

And I just thought, so basically the last time we, you were on the podcast, we didn't talk about DSI that much, but you have actually some background in IR, you care about IR. No, I don't care about IR, but, but I think DSI, okay, like DSI or, or generative retrieval was like, I think one of my favorite works in the old of like, uh...

I, I, I have some IR background in, like when I was doing a PhD, I did some rec- REXes work. I did some retrieval work with REXes and stuff like that. So I have some IR and REXes background. So I think generative retrieval and generative REXes is re- all very conflated. DSI started as a retrieval thing, so we did like natural questions like ranking of like documents- Learn the index. Yeah...

like doc- documents that re- everything. The, it started off as, I mean, that's actually we did an interview with Yannic, like me and Dawn, we did interview when, when the paper came out, like long time ago. So at that time we wanted to like reimagine retrieval and search. So we wanted, at that time, LLM, we were still using T5 models at that time. It was like not, we were not in the LLM era yet. It was pre-LLM era.

It was like, okay, pre-training works kind of thing. And then there were like some pre-trained models around. So we wanted to re- reimagine retrieval, right? But retrieval REXes, they're all the same formulation, ranking, retrieval problem, right? And then that's where we started to imagine retrieval as one giant RLM that, that encodes everything in the memory.

But we tried so many different, like semantic ideas. Actually, my collaborator, Vin, was the one that came up with idea, semantic idea- ideas that basically... At the start of this whole generative retrieval was actually basically literally just trying to give a document like identifier and just predicting like raw brute force predicting like this. It actually works. The ID.

Like, it actually works because the models can memorize some- something. If you look at the literature from all the way to things like Doc2vec or this very of like model, the words have no meaning. They're just ID in, in a vocab, right? It's another's number, right? And technically the models should have enough capacity to pr- predict.

But I think semantic IDs was an idea that, that basically you have some like semantic association, and then you actually try to break down the subspace hierarchically, right? So how this would evolve in the REXes was at that time after DSI came out, right? So at Chi's group and, uh, Mahesh, the guy who, who they did some exploration of applying DSI to, to REXes, and that's how that generative, generative REXes recommended system paper came out. Uh, that- Yeah.

I didn't even know he was involved. It's crazy That was like basically us like transferring this like basically, okay, DSI works. We, we try to try it on, on REXes. And then I think the recommended system people have a slightly different way of doing semantic IDs, but it's basically just because the pro- the domain is slightly different.

But after that, I think we were done with the invention part, and we just, like- [laughs] The rest is details. The, the, the rest are, are details. So over time, I also left Google and stuff like, so over time these things evolve a little bit here, there. I think I also saw like something like Spotify is also using something like YouTube, Spotify, like they, they use this type of like semantic IDs, this type of DSI like models.

I think from the research community point of view, like the DSI work was the first one that like decode semantic tokens. But then when we went to, I don't know, academic community is like strange in a way that like they will do things like, oh, this is generative retrieval. It's not generative REXes.

It's like they will do this type of like random things that is like a, a bit strange. But yeah, I mean, this was like the whole history of this generative retrieval. Apparently, there's also a lot in, like of people working on... I don't follow, actually, I don't follow at this at all now. It's just not even in my mind. Yeah.

But there's, there was once I went to, even in the S- Singapore office, there are Swis actually like working on- Generative retrieval. They don't work on-- I don't know whether they're still working on it, but I met a person that tried to explain generative retrieval to me. It was quite funny that, you know, I kind of like co-invented generative retrieval. But yeah, I think this is-- this whole IR thing has been-- it's just an interesting phase.

And I, I definitely think DSI is one of my more creative works that I've done that is not like really LM, but it's like- Under the general principle of apply ML to everything, if a Googler is working on generative retrieval, would that be like AI overviews? Is that s-something similar? I have no idea. Okay. For the people listening, I, I did have a track there. I think you just type in AI.engineer and you'll get it, where the Gemini guy was talking...

Sorry, the YouTube guy was talking about how they use Gemini for their Raxis. I don't know what size of Gemini, 'cause they-- he didn't talk about it. But this is public work now, and basically every YouTube video uploaded gets encoded into some kind of codebook, and they, they retrain this every da- on some kind of batch job. Ah. Yeah. Just interesting.

So yeah, I, I don't know if you even know [chuckles] what AI-- what Gemini is being used for. [chuckles] I, I don't follow like this- Yeah, yeah... these days. I do think like, in the sense of like for people who are not-- still not getting it, applying intelligence and the, the general intelligence of an LLM to the retrieval, to the recommendation task means you can accommodate such weird recommendations, like such weird queries as well, that normally, like no classical system can ever handle.

And I think like it's also somewhat emergent in the sense that when you were using T5, you just couldn't actually add that much value on top of a normal BM25 retrieval technique. Would, would you say that's accurate? It is not, it's not just about paraphrasing, it is about understanding query intent. I-- BM25 is a really strong baseline, actually. At least. BM25 is a really strong baseline, yeah. Uh- Is that like, unlike I, I...

Sorry, so I don't, I don't know the, the, the, the comparative delta v-versus T5 for you guys versus BM25, but I don't expect it to be very high, and I expect it to be a lot higher for, for a true LLM based Raxis, depending obviously on the query set. I didn't really think about it this way before, but because I've done modeling in many different domains, including like search and, you know, in the search commun- IR community, there's also set of benchmarks and stuff like that for like, that people will climb on and some like, there's Amy of, or like a, of...

I don't know what is it c-called these days anymore, but generally the modeling dynamics of IR tasks are-- is very different from, like in Raxis tasks, is very different from standard language tasks or like vision tasks and something like the way we kill climb LLM, where you train models into, like the way that modeling things interact with this environment is very different.

So I think that I honestly, I hated working on like Raxis and retrieval stuff. Okay, I'm just talking about old days. When you work on like T5, you work on-- you change, change architecture, you try to improve perplexity, you use SuperGLUE, like this is the olden days. You are on, like even now, when you train LLMs, you just do zero-shot, few-shot, stuff like that. Your things-- the-- you-- 'cause as a researcher engineer, you just interact with the environment a lot by this.

You're just like, okay, RL by this environment. But Raxis and IR has a very strange feeling to it. Strange feeling in the sense that it feels like you're-- the-- like whatever works-- it's like you are in this, in a world with the, the gravity is different, or like you are in a world where the modeling things that feel intuitive are not intuitive.

So, so it feels like a very strange space to... So I wrote some papers back in my days on like Raxis and stuff like that. Every time I ran some modeling experiments for, for Raxis and stuff like that, I, I didn't enjoy the... It, it feels like the environment was rude, and it feels like the, the vibes are just like, like- What makes it rude? It just feels- Transactional? No, no, not, not, not, not, not, not transactional.

Like, I, I don't know how to describe it. For example, like if you play like sports, like play tennis, badminton, where you hit the ball, you have a very nice feeling like hitting the sweet spot. When you do modeling in traditional dome, when you get the feedback back, you feel like everything sounds right, everything feels right, everything right.

But Raxis and IR is like a problem where it's like you hit the shuttlecock, and you hear a glass shatter like randomly. It's like you just feel this weird- I see... world that, that- Cause and effect are too far apart... like, like it just feels strange. And then sometimes maybe the metric like, I think Raxis, they use like all the NGCG effects, and then the BM25 is strong, and then you do this, and then you get like worse than the like the, the BM25.

Back in the day when you stack two LSTM to three LSTM, you were like, "Whoa, I see life." It's just the game- It's an unrewarding area to work in. It's, it's just weird. Also, the IR community and the retrieval community is also like always behind the mainstream, and then now it's just probably gotten even more worse because of LLM and stuff.

So, okay, I'm getting into hot tech territory, but it's just like certain conferences are just like behind NeurIPS and ICML and stuff like that. Yeah, some conferences are just like, they're just like applying things that- [chuckles]... that this- They're downstream of- They're downstream. Right. They're downstream. Okay.

So it always feels very uninspiring to, to, to, to work on, on this. Yeah. Look, there's a reason that you left and- But it was like a side quest, like where I work on as a side quest thing. Yeah. Uh, yeah. Okay, un-understand. I still think it's an important business problem, even though maybe it's an unre-unrewarding field. You can understand why.

Because the academic benchmarks for those tasks are just so far detached from- Yeah... they're so far detached from what industry is. I didn't work on any of these like in, in like- Yeah, yeah... like the, the, the thing, but just from an academic point of view. Oh, then all you need are online evaluators, right? Yeah, yeah, D-test and like, oh, ooh, okay. That would have been a different experience, yeah.

That is mostly our sort of topic, research topics, coverage and everything. I think we're just gonna end on a very simple one on GDM Singapore. You, you organize a symposium here with Roger FT, Kwok, and all the others. Basically, what's the general message or the, uh, impetus for starting GDM Singapore? So we talk about the event first. So the event was mostly...

So Kwok and I are going to start the team, and then I think before I came back, we discussed this for some time. Jeff was very supportive of this. He was in the region many times in Vietnam and Singapore last like, uh- About around the time when I was going to come back. And I think that-- So this event itself was Quoc and Jeff was, were visiting, and we just inspire the community here.

I think that it's also a bit more like a soft, like setting the tone right for the start of the Gemini team in Singapore. And I think it's a very rare instance where you get somebody like Jeff and Quoc Hoa, the true pioneers of AI i- in the world, to be in one room. And then are you there as well? And I think, like many people told me that- Not a true pioneer of AI, but okay. [laughs] I was there to, to live tweet. Yeah, yeah.

I think having them all in one room and then giving these talks, like many people came up to me and said they were very inspired by their presence in the region. So starting a team and starting something is also very-- Like, there's also no one moment. There is no, "Okay, I press the button, it starts," right? It's like a process, right?

So we hire people, and then people join one by one and stuff, stuff, stuff, something like that, right? So I think this event was more, I would say like a, to set the vibe. I think it's possible for Singapore to be close to the frontier, and I think that with having the true pioneers of AI here, we want to give this, like basically more like an inspiring thing.

And also get-- Like for Quoc and Jeff to meet the people here and then-- So Jeff was here last year, but Quoc hasn't been here for some time, and he's gonna have team here, so it's like also nice to bring him around and meet the people here. Yeah. So it was a really, like amazing event. We met Long as well, and- Who a lot of people don't know has a CS degree. He's like one of the few PMs with a CS degree. Yeah, yeah.

I would say that the context of the meeting was more like partially like also he, he wanted to learn more about the IMO stuff. Oh, really? [laughs] And then also about Jeff. He want- Oh, 'cause he, they invited you without knowing that these guys were coming or something like that, right? [laughs] I was a bit, so like, like some, yeah, Jeff and Quoc and me were-- we went to visit, uh, chat with at the Istana. And we discussed a little bit on deep thing, discussed a bit on IMO.

And then I think the rest of it was more like Jeff and Lisa Long was talking more about like-- Be- became less about AI and tech, more about very macro economic or political thing. Which was I was very out of element with, so I was just like, as, I just- You were in a suit. Mm. I, I was just talking about the deep thing and the IMO and stuff like that. Yeah.

But he seemed genuinely quite surprised that AI has reached this point. So I think-- But it was a, was an interesting, uh- Yeah. I, I would say for people, like you have done something that is unique in Singapore's history so far. Like you are, you're establishing a frontier research lab in Singapore, which is an accomplishment.

I think the other thing also that I guess I'm still trying to wrap my head around is, does geography actually matter? Like you're all working on the team. You have your London people, you have your Mountain View people, and mostly you're just like collaborating with them anyway. You've collaborated with them your whole life.

I don't e- don't even really know what countries mean anymore when it comes to research or just AI in general, because this thing is just inherently international from the start. This is a very good, like question. Also, it's related to the thing about identity, right? Because I think you also moved from SF and Singapore quite a bit, right?

I was in Mou- like Mountain View this like one, two weeks ago, and like I'm here, but like almost all my, like my-- If you just look at my sibling, aside from my family, like everybody I talk to is like somehow in the Bay Area or like just because of work and everything. I think the geog- geography matters.

Okay, firstly, the, the most like thing is like logistical is like probably the time zone. So you literally want the twenty-four-hour coverage around the world. No, there are, there- Sun doesn't set... there, there are advantage-- No, what, what I'm saying is that the difference is possibly for like, it's also more like, like people define the location more than the location define- Okay... the people somehow.

Okay, the time zone, we get to the time zone a little bit. Like, like there are pros and cons. I think there's pros and cons, right? So you bullish on Sing- on Asia, Singapore people, the talent pool? I think we managed to find like really amaz- amazing people. But I would also have to say that this type of things is more like talent attract talent. I think most of the time, like people are very excited.

Like the vibe I get is that people are very excited because it's like Quoc's team and my team, and we're working on like very core things related to, to, to AGI. So I feel like the talent we can get from the region is re- really good, but it's only, it's only because it's us as that we can unlock this talent, otherwise might join some other place like that.

Yes, and move to, to the US. Yeah, yeah. About the identity-wise, I would say that I definitely agree with you that, like why it doesn't matter. Like that-- I think that the advantages of S- Singapore itself or like just anywhere, like it's also that you are like, okay, the world is very global, so technically you can interact as much as you want.

You can also move there. But I think Singapore has this advantage where you can go close, and you can go far. I think the Bay Area is like so much about... I have s- friends in London and New York that would just never move to the Bay Area. So I'm not against Bay Area. I think Bay Area is a great place, right? But it's just AI, AI, AI, AI everywhere, right?

I think sometimes if you have some like mental space and energy to like, to have some other culture and then like, you know, like London, Singapore, New York, they have their own culture, right? But the Bay Area culture is just like AI, right? [laughs] Like you just go anywhere, you just hear AI everywhere, right? Even the billboards and stuff like that. You can hear a bit much. Yeah.

Although I did see some billboards down here- Oh, AI also? [laughs] Yeah. No, I was like, "What is this?" It's, it's- It's culture infecting Singapore. [laughs] I do think that to some extent, if you want to do research in, you need a little bit of peace and quiet somewhere, right? So this island may be good for that, but then you can-- You still, you're still like able to m- like be connected, right? Okay. So I think that's mainly... Talent-wise, I think people are strong here. Uh, yeah.

So far enough away, but you're still connected. You have strong talent. What are you hiring for? You're still hiring, right? We're hiring, like, like my team will work on like RL and reasoning for Gemini and Gemini deep thing. I think we care more about like talent density now, so we're not like also like growing that big, this small Team first just because co-compute per capital is probably, like, important.

And yeah, so I think that's something that, that we're hiring for now. Basically, I personally just... There's a lot at the ends, but I think, like, generally there's a lot of people, like, who are very capable, but I think what I'm looking for mainly is either you have, like, a track record of RL research or, like, some...

Even not necessarily the RL, but, or, or like some exceptional achievement in, like, coding competitions or, like, some exceptional achievement somewhere, then that's, like, the kind of people that, like, we, we want. Yeah. 'Cause you don't strictly require... I do remember something about your record days where you're like, you like to train your own trainings from scratch, right?

So you don't- I forgot if I said that or not, but [laughs] to some extent, to, to, to some extent, yeah, I think we'll, we'll definitely be very happy with people that are, like, very high stats and just, like, even without much- Statistical knowledge? No, no, stats. Yeah, yeah. Just stat points. Child with int. Like, just high tech- [laughs]... stat points people. Like just raw- IQ, yeah... like, ta- talent people like this.

I think all, like, strong engineering skills, ML. ML can be learned easily or the, or AI knowledge can be learned easily. Yeah. I think maybe one version of this is can it be done on a student budget, right? Where they-- wait, can you do something, and interesting, anymore on a student budget? I would say rele- relevant to the point where conferences are quieter these days.

I did do an inter-interview with one of the best paper winners where they worked on 1,000-layer neural network RL, and that was done on a student budget. They-- It was very cleanly executed pieces and paper and, and good findings. Look, I'm not sure if production models will ever go to the 1,000 layers, but they stretched it in an interesting direction and found some good recommendations, and the guy immediately got hired by OpenAI.

And I, I think that's encouraging for the grad students in the market who are like, "Okay, well, do I need to know somebody who works at these labs in order to get in? My uncle works there. I get the internship" or whatever. No, actually you can just do it on a student budget with, with good advisors.

Oh, I actually think one thing interesting is that for most of the people that I actually went to recruit them, like, personally, right? So it's... You, you, you see their work, and then you send them DMs, right? So I get a lot of DMs- For your best paper? No, no, no. Like, like, for hiring generally. Oh. So generally I almost...

Like, and to your point, it's like you almost-- like, you can just do good work, put it online, and then somebody will contact you, right? It's actually super easy but super hard at the same time because- No, I, I can tell you, like, I talked to a few of these grad students. They don't know what good work means, right? Because they don't know...

There's so many things. Their professors have the agenda they're forcing on them, which, like, may not be right, because it's not like their professors know what to work on either. So yeah, they just need guidance. They just need, like, "Hey, work on these, like, five things. Show me interesting result in any of them."

Okay, so if somebody comes up with something and then does something that you feel that is very tasteful and aligns with what, like, researchers in the labs like, like want- Mm-hmm... and they come up with that independently, you know that the function that produces the result- Immediately... is good, right?

Like, if you just go and tell somebody to do this, like, you, you, you get-- you just get the signal that these guys can execute, right? So I think there's some value in people that- Yeah, they demonstrate taste, research taste. Yeah. Re- Research taste. Yeah. It's very interesting, and I feel like I could give people... Yeah, I, I do care about this.

In some ways, the research directions work that I do is k- a little bit of that. Like, it's low accountability for me because obviously it's just thought experiments. But I think for a lot of people it's like their career is bounded by can you demonstrate research taste with this, like, short three, four years of that you have and just do it. [laughs] Yeah.

Yeah, I would say that this is-- it's more there's so much competition just because of, like, the-- everybody wants to get into AI. It's just more of, like, how to... Like, mostly it's more, like, how you gonna prove yourself that you're, you're- Mm. Right. Yeah. It must be hard these days to be a grad student trying to prove yourself. It's definitely harder, but yeah. Yeah.

Not your job. Okay. That was it. Do you have any other sort of rants or topics that you had queued up before we wrap? No, I don't. Yeah, but it was great. Yeah, it was. I had a great time. It's fun chatting, man. Fun, fun chatting. Yeah. You know, even-- I even love-- Last time we were supposed to do-- Last time we meet at the symposium, we were supposed to record. But even we just ended up hanging out and chatting.

It's just nice to get the brain dump of what's going on in your world, 'cause, yeah, working on really important stuff, man. Always great to chat with you when I see you. Yeah. Good to chat. Parting words on the sort of weight loss and workout journey, 'cause that's also a big thing for you. I think being healthy is important to be-- to do good research, right?

And I think, I think I've been probably in one of-- I'm probably in the peak physical health now. Yeah, you look great. Yeah. Thanks. And I think it's also impacted my work in a good way. You did the sort of Karpaty inspired, like, biohacking. I didn't go to extreme, but I was- [laughs]... quite, also quite data-driven when I came, c- c- kind of. I will, like, have my own email, and I will track this.

The-- I was-- I'm still supposed to make a blog post about this, but I, I feel like I'm not, like, really at the endgame yet. So, like, when I get there or... But yeah, just to, just for people who don't know, like, I think I lost 23 k- kilos. This year. Actually across one year. Yeah. One half years. Yeah. Uh, so 23. Yeah, basic- literally from the last podcast to now. Yes.

Yeah. Yeah. It's, that's the abolition study now. [laughs] 23 kilos. Yeah. And I think, like, my HRV, heart rate variability, has went up by two, two times, and my resting heart rate has dropped by 30 beats per minute. 30 beats per minute? Like, it was like 80, 90, and now it's 160. Oh, yeah, 80, 90 is super high. Yeah. I was unhealthy. Yeah. Yeah. Yeah. [laughs] Okay. Yeah.

So I think, I think it's like when it's hard, like, what-- do you have a thing that kept you going, you know? A lot of people, they maybe, they, they're focused on AI, and including myself, right? I do prioritize work. I enjoy work. I don't enjoy the fitness side. But I-- obviously it, it feeds into your intellectual work, like, to sort of log off and go for a walk, eat better, all that kind of stuff. It obviously feeds in.

But, like, people seeing a positive example like you, they will get inspired to do the same thing. So I think it's good to set yourself up as an example. I think definitely helps. Like, when I do these things for my health, I just think that it's also part of work because it helps me to get better at my job. Yeah, yeah. So it's important as well. I think it's important as well. Yeah.

I like the HRV off the bat. I have no idea what mine is, but yeah. There's a general question about what is productivity and how do you measure it, what, what really matters, and it's still unclear to me. But I do think general energy level and hunger almost. Like, you almost have to, like, experience physical hunger in order to have intellectual hunger. And I don't know if that's like [laughs] a thing.

No, when I'm hungry I just think of food. [laughs] I think to me it's like these disjoint things. But when you... When, when... But it's hard to do work when you're hungry. Yeah. Okay. Thank you so much. Yeah, thanks. It was really great. Yeah, have a great time. [outro music]

Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay

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