natolambert/rlhf-book resource

Health v2 · maintenance only

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2345 stars · 254 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source textbook on Reinforcement Learning from Human Feedback (RLHF) and LLM post-training, available online and in print, with companion reference code implementations. It covers instruction tuning, reward modeling, policy gradients (PPO, GRPO, DPO), rejection sampling, and distillation.

Use cases

• learn how RLHF works from first principles
• understand LLM post-training techniques like DPO and PPO
• study reference implementations of reward model training
• prepare for a career in LLM alignment or fine-tuning
• find a canonical reference for rejection sampling and GRPO
• teach a course on language model alignment

When to choose

• you want a comprehensive, curated introduction to RLHF and post-training
• you need runnable reference code for RLHF algorithms
• you prefer learning from a structured book with a community and course materials

When to avoid

• you need a production-ready RLHF training framework
• you want a maintained software library with API stability guarantees
• you need hands-on tooling rather than conceptual explanations

Facets

learning-resource · maturity active

machine-learning llm-training developer-tools large-language-models machine-learning tutorials education python cross-platform rlhf post-training textbook reinforcement-learning preference-learning open-source-book

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markdown · JSON · MCP: product_card(name="natolambert/rlhf-book")