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online iterative RLHF
webrlhfbook.com·rlhfbook.com/
An open online textbook on RLHF, useful for researchers and practitioners seeking a structured introduction to human feedback-based alignment techniques, including the iterative online variants used in modern LLM training pipelines.
Metadata
Importance: 62/100bookeducational
Summary
An online textbook dedicated to Reinforcement Learning from Human Feedback (RLHF), covering the theory, methods, and practical implementation of training AI systems using human preference feedback. It focuses particularly on online and iterative RLHF approaches used to align large language models with human values and intentions.
Key Points
- •Comprehensive coverage of RLHF methodology including reward modeling, preference learning, and policy optimization
- •Emphasizes online iterative RLHF, where the model is updated continuously as new human feedback is collected
- •Bridges theoretical foundations and practical implementation for aligning LLMs using human feedback
- •Relevant to understanding how models like ChatGPT and Claude are fine-tuned for safety and helpfulness
- •Serves as a reference for researchers and practitioners working on alignment via human supervision
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| Reward Modeling | Approach | 55.0 |
| RLHF | Research Area | 63.0 |
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Reinforcement Learning from Human Feedback -->
RLHF Book by Nathan Lambert
Welcome to the Course
Video introduction and overview of the RLHF Book
Watch
Changelog
Last built: 08 April 2026
April 2026 : Final editorial polish for print — ported Manning edition improvements, clarity pass on equations and terminology, typo/grammar fixes across all chapters, product chapter expansions. The book is heading to print, so expect fewer content changes going forward.
March 2026 : Launch course page with lecture videos; PDF syntax highlighting; product chapter expansions (Ch. 17).
February 2026 : v2 content: direct alignment chapter, new diagrams, RL cheatsheet, appendices, search bar, Kindle support, editor fixes.
January 2026 : Major chapter reorganization to match Manning book structure; code examples library; old URLs redirect to new locations.
December 2025 : Working on v2 of the book based on editors feedback! Do check back for updates!
November 2025 : Manning preorder available.
July 2025 : Add tool use chapter (see PR )
June 2025 : v1.1. Lots of RLVR/reasoning improvements (see PR )
April 2025 : Finish v0; overoptimization, open questions, etc.; evaluation section; RLHF x Product research, improving website, reasoning section.
March 2025 : Improving policy gradient section; finish DPO, major cleaning; start DPO chapter, improve intro.
February 2025 : Improve SEO, add IFT chapter; RM additions, preference data, policy gradient finalization; PPO and GAE; added changelog, revamped introduction.
Acknowledgements
I would like to thank the following people who helped me directly with this project: Costa Huang, Ross Taylor, Hamish Ivison, John Schulman, Valentina Pyatkin, Daniel Han, Shane Gu, Joanne Jang, LJ Miranda, Sharan Maiya, Andrew Carr, Cameron R. Wolfe, and others in my RL sphere (and of course Claude).
Additionally, thank you to the contributors on GitHub who helped improve this project.
Citation
If you found this useful for your research, please cite it!
@book{rlhf2026lambert,
author = {Nathan Lambert},
title = {Reinforcement Learning from Human Feedback},
year = {2026},
publisher = {Online},
url = {https://rlhfbook.com}
}Resource ID:
ebcbaba2d260e656 | Stable ID: MmI0NDE1OW