RLHF Book chapter breaks down PPO, GRPO and REINFORCE for LLM post-training
serrjoa · x · 2026-07-29
The RLHF Book’s chapter walks through policy-gradient methods used in post-training for language models, explaining how reward-model feedback drives weight updates and how algorithms like PPO, GRPO, and REINFORCE differ.
- Covers the RLHF training loop from prompts to reward scores to optimizer steps.
- Explains why policy-gradient methods became central to RLHF for LLMs.
- Highlights the trade-offs among modern algorithms and notes that data quality is often the biggest factor in success.
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