Nathan Lambert Releases RLHF Textbook on LLM Post-Training
Interconnects (Nathan Lambert) · rss · 2026-08-10
AI researcher Nathan Lambert announced the release of his new book, Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs, published by Manning and freely available online.
Key Highlights:
- Intuition & History: Explains why post-training works in accessible terms, tracing the evolution from early preference learning to the ChatGPT era.
- RL Algorithms: Provides mathematical intuition and system design insights for Policy Gradient, PPO, GRPO, GSPO, and more.
- Systems Challenges: Discusses modern RL systems-level hurdles like asynchronous training and train-inference mismatches.
- Demystifying Distillation: Breaks down industry-standard practices for knowledge distillation, clarifying common misconceptions.
Tailored for developers and researchers with a CS background, the book aims to help readers build a comprehensive worldview on post-training.
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