Reinforcement Learning from Human Feedback
A comprehensive public guide details how to train AI using human preferences.
Deep Dive
A major open-source book on Reinforcement Learning from Human Feedback (RLHF) has been reorganized and updated. The guide, which aligns with a Manning publication, covers core techniques like Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). Recent chapters added in 2025 include tool use and improved reasoning sections. The project acknowledges key contributors from the AI research community and continues to be actively developed based on editorial feedback.
Why It Matters
This resource makes advanced AI training methods accessible, accelerating development of safer and more helpful models.