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This paper identifies oversensitivity in continuous reward models for reinforcement learning, where equally good responses receive different scores, and proposes a discretization technique using Monte Carlo dropout to reduce this oversensitivity while maintaining discriminative ability, leading to better policies and less reward hacking.