Alignment ForumArticle
Debate Training Reduces Reward Hacking in RLAIF
Reward hacking against judge models is a known failure mode for anyone doing RLHF or RLAIF on fuzzy tasks like code maintainability or tone. This gives a concrete mitigation, debate-style adversarial checks, that's worth prototyping before scaling judge-based reward pipelines further. It's early research, not a production recipe, but the direction is credible given the source team.