An expert overview of post-training techniques for language models, covering the entire workflow from data generation and curation to advanced algorithms like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), along with practical advice on evaluation and iteration.
A comprehensive overview of the entire process behind building Olmo 3 Think, covering the full stack from pre-training architecture and data selection to the detailed post-training recipe involving SFT, DPO, and a deep dive into the advanced infrastructure for scaling Reinforcement Learning (RL). The summary also includes critical reflections on the challenges and nuances of evaluating modern reasoning models.