In this episode of The Cognitive Revolution, we dive deep into frontier post-training techniques for large language models with Nathan Lambert from the Allen Institute for AI. Nathan discusses the groundbreaking Tulu 3 release, which matches Meta's post-training performance using the LlAMA base model. We explore supervised fine-tuning, preference-based reinforcement learning, and the innovative reinforcement learning from verifiable reward technique. Nathan provides unprecedented insights into the practical aspects of model development, compute requirements, and data generation strategies. This technically rich conversation illuminates previously opaque aspects of LLM development, achieved by a small team of 10-15 people. Join us for one of our most detailed and valuable discussions on state-of-the-art AI model development.
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CHAPTERS:
(00:00:00) Teaser
(00:00:59) Sponsors: Incogni
(00:02:20) About the Episode
(00:05:56) Introducing AI2
(00:09:56) Tulu: Deep Dive (Part 1)
(00:17:43) Sponsors: Shopify | Oracle Cloud Infrastructure (OCI)
(00:20:38) Open vs. Closed Recipes
(00:29:48) Compute & Value (Part 1)
(00:34:22) Sponsors: 80,000 Hours | Notion
(00:37:02) Compute & Value (Part 2)
(00:42:41) Model Weight Evolution
(00:53:16) DPO vs. PPO
(01:06:36) Project Trajectory
(01:20:39) Synthetic Data & LLM Judge
(01:27:39) Verifiable RL
(01:38:17) Advice for Practitioners
(01:44:01) Open Source vs. Closed
(01:49:18) Outro
Everything You Wanted to Know About LLM Post-Training, with Nathan Lambert of Allen Institute for AI | "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis podcast - Listen or read transcript on Metacast
