New Book Explores Reinforcement Learning from Human Feedback

Nathan Lambert has completed his book, "Reinforcement Learning from Human Feedback," which aims to provide a foundational resource for fine-tuning, aligning, and post-training models like ChatGPT. The book, developed over nights and weekends since 2024, incorporates insights from building the Olmo model. It will be released with a comprehensive 10-hour course, including slides, functional code, and an example model completions library, alongside a free online version. Physical copies are expected to ship from Manning and Amazon within a few weeks. AI

IMPACT Provides a new educational resource for practitioners working with large language models and alignment techniques.

RANK_REASON The item describes the completion of a book on a specific AI research topic. [lever_c_demoted from research: ic=1 ai=1.0]

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• ChatGPT
• Manning
• Nathan Lambert
• Olmo
• reinforcement learning from human feedback

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