Reinforcement learning from human feedback (RLHF) has become an important technical and storytelling tool to deploy the latest machine learning systems. In this book, we hope to give a gentle introduction to the core methods for people with some level of quantitative background.
AI summary
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human preferences to train AI models, especially Large Language Models (LLMs), to align their outputs with human values and goals, moving beyond simple programmed rewards by having humans rank or rate AI-generated responses to teach a "reward model," which then guides the AI's policy using reinforcement learning to produce more helpful, harmless, and honest results. This process typically involves three stages: supervised fine-tuning (SFT), training a reward model (RM) from human comparisons, and optimizing the AI (policy) with Proximal Policy Optimization (PPO) using the RM's scores.
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