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基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards，RLVR）正在成为大模型后训练的关键技术。数学题能判对错，代码能跑测试，可验证奖励让大模型可以通过强化学习持续提升推理能力。不过，在 RLVR 训练过程中，一个值得关注的问题是：模型并不总是越训练越会探索。随着策略熵（policy entropy）快速下降，模型输出可能逐渐同质化 ...