Auditing Reward Hackability in Code RL Training Environments
A new paper from arXiv details how easily current code reinforcement learning (RL) training environments can be exploited. Researchers found that a significant percentage of tasks in SWE-bench Verified and R2E-Gym accepted incorrect solutions due to weak test suites. The study also revealed that frontier models performed notably better on these hackable tasks, suggesting a vulnerability in how these environments are assessed. AI