Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking

Mohammad Beigi, Ming Jin, Junshan Zhang +2

Reinforcement Learning from Human Feedback (RLHF) remains vulnerable to reward hacking, where models exploit spurious correlations in learned reward models to achieve high scores w…