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EvoRS: On-Policy Self-Evolution of Reward Systems for Open-Ended Reinforcement Learning
Open-ended reinforcement learning often relies on rubric-based rewards for tasks without directly verifiable answers. Yet the policy and reward system form a dynamic feedback loop: as the policy optimizes the current reward, an initially useful reward system may become unreliable due to reward hacki
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