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Shownotes
It’s finally here! The public (and most complete) version of my talk covering every stage of the process to build Olmo 3 (interconnects.ai/p/olmo-3-americas-truly-open-…) Think (slides (docs.google.com/presentation/d/1rhXAPyaflqi5cB…) are available). I’ve been giving this, improving it, and getting great feedback at other venues such as The Conference on Language Modeling (COLM) & The PyTorch Conference.This involves changes and new considerations of every angle of the stack, from pretraining, evaluation, and of course post-training.
Most of the talk focuses on reinforcement learning infrastructure and evaluating reasoning models, with quick comments on every training stage. I hope you enjoy it, and let us know what to improve in the future!
Chapters
* Introduction
* Pretraining Architecture
* Midtraining Data
* Long-context Necessity
* Building SFT Data
* Reasoning DPO Surprises
* Scaling RL
* Evaluation Overview
* Evaluation Reflections
* Conclusions
Here’s the YouTube link:
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit interconnects.ai/subscribe?utm_medium=podcast&…