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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

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Optimizing Language Model Training

This chapter explores the intricate post-training processes for large language models, contrasting open-source and closed-company methodologies. It emphasizes the significance of human feedback in instruction and preference tuning, as well as the technical challenges that affect model performance. The discussion further covers the computational aspects, cost efficiencies, and advancements in reinforcement learning that enhance the models' capabilities in handling various tasks.

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