Innovations at the Allen Institute for AI

This chapter explores the Allen Institute for AI's history, leadership changes, and focus on open language models, including the significance of storytelling in AI initiatives. It also discusses the Tulu project, aiming to enhance post-training techniques and improve model performance while emphasizing transparency and innovative approaches in a competitive landscape.

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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

https://www.interconnects.ai

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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