Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach to aligning AI systems with human values. By combining reinforcement learning with human input, RLHF has become a critical methodology for improving the safety and reliability of large language models (LLMs).

This book begins with the foundations of reinforcement learning, including key algorithms such as proximal policy optimization, and shows how reward models integrate human preferences to fine-tune AI behavior. You’ll gain a practical understanding of how RLHF optimizes model parameters to better match real-world needs.

Beyond theory, you’ll explore strategies for collecting preference data, training reward models, and enhancing LLM fine-tuning workflows. Common challenges such as cost, bias, and scalability are addressed with practical solutions and AI-driven alternatives.

The final chapters cover emerging methods, advanced evaluation, and AI safety. By the end, you’ll be equipped with the knowledge and skills to apply RLHF across domains, building AI systems that are powerful, trustworthy, and aligned with human values.

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Spis treści

A Practical Guide to Reinforcement Learning from Human Feedback. Using Human Signals to Align AI Models eBook -- spis treści

• 1. Introduction to Reinforcement Learning
• 2. Role of Human Feedback in Reinforcement Learning
• 3. Reward Modeling
• 4. Policy Training Based on Reward Model
• 5. Introduction to Language Models and Fine Tuning
• 6. Parameter Efficient Fine Tuning
• 7. Reward Modeling for Language Model Tuning
• 8. Reinforcement Learning for Tuning Language Models
• 9. Challenges of Reinforcement Learning with Human Feedback
• 10. Direct Preference Optimization
• 11. RLHF and Model Evaluations
• 12. Other Applications