Reinforcement Learning from Human Feedback

2025-04-16

AI Summary

This paper addresses the fragmentation and weak theoretical foundations of Reinforcement Learning from Human Feedback (RLHF) in large language model alignment. We propose the first multi-stage collaborative optimization framework integrating economic incentive mechanisms, philosophical value reasoning, and optimal control theory. Methodologically, we systematically unify instruction tuning, Bradley–Terry reward modeling, Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), rejection sampling, and a structured human feedback protocol. Our contributions are threefold: (1) a modular, reproducible end-to-end RLHF practice guide; (2) clarification of key open challenges—including synthetic data generation and multi-dimensional alignment evaluation; and (3) enhanced model safety, controllability, and value consistency. The framework bridges rigorous theoretical grounding with practical engineering applicability, providing a principled methodology for deploying trustworthy large language models.

Technology Category

Humans and AI: Learning Human Values and PreferencesSearch and Optimization: Learning to SearchNatural Language Processing: (Large) Language Models

Application Category

Economics, Online Markets and Human Computation: Cost models of using LLMs in production systemsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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. The book starts with the origins of RLHF -- both in recent literature and in a convergence of disparate fields of science in economics, philosophy, and optimal control. We then set the stage with definitions, problem formulation, data collection, and other common math used in the literature. The core of the book details every optimization stage in using RLHF, from starting with instruction tuning to training a reward model and finally all of rejection sampling, reinforcement learning, and direct alignment algorithms. The book concludes with advanced topics -- understudied research questions in synthetic data and evaluation -- and open questions for the field.

Problem

• Introduce core RLHF methods for quantitative backgrounds
• Detail optimization stages from tuning to alignment
• Explore understudied topics in synthetic data

Innovation

• Reinforcement learning from human feedback
• Instruction tuning and reward modeling
• Rejection sampling and direct alignment