Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking.

arXiv:2602.01750v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) remains vulnerable to reward hacking, where models exploit spurious correlations in learned reward models to achieve high scores while violating human intent.