Xuekang Wang

Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning

CHERRL is introduced, a Controllable Hacking Environment for Rubric-based RL that enables stable reproduction of reward hacking, explicit observation of reward divergence, and identification of hacking onset and analyzes different judge biases from the perspectives of discoverability and exploitability.