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cs.AI2026
Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization
Mohammad Beigi, Ming Jin, Lifu Huang
Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what proxy RL teaches before that…
cs.AI2026
IR: Contrastive Inverse Reinforcement Learning for Interpretable Detection and Mitigation of Reward Hacking
Mohammad Beigi, Ming Jin, Junshan Zhang +3
Reinforcement Learning from Human Feedback (RLHF) enables powerful LLM alignment but can introduce reward hacking - models exploit spurious correlations in proxy rewards without ge…
cs.AI2026
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…