Exploring Post-Training Dimensions and Model Character Development

This chapter explores the intricacies of post-training processes for language models, highlighting the importance of hyperparameter fine-tuning and evaluation feedback. It also discusses the challenges of aligning academic objectives with organizational expectations while ensuring a consistent character in models for diverse users.

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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.

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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