Intro

This chapter delves into the latest advancements in large language model post-training techniques, emphasizing the significance of improving data and pipelines. It also discusses innovative methods like reinforcement learning and highlights the challenges presented by human-generated preference data within the context of collaborative research efforts.

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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.

Check out Nathan's Lambert newsletter:

https://www.natolambert.com

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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