Secrets of RLHF in LLMs

PPO internals exposed: Documents critical implementation details (reward normalization, advantage estimation, KL penalty scaling) that aren't in the original papers but make or break training.

Empirical ablations: Systematically studies which PPO components matter most, providing practical guidance for RLHF practitioners.

Open-source code: Releases a clean reference implementation that others can use to reproduce and iterate on RLHF.

RLHF demystification: Part of a broader 2023 wave demystifying RLHF, preparing the ground for simpler alternatives like DPO that arrived later that year.