大模型靠自己判题能持续进化吗？实验发现能短期提升，但长期会崩溃。
Can Large Reasoning Models Self-Train?
- 用多数投票让模型自我反馈，实现持续迭代优化
- 自训练初期性能提升，但长期导致奖励欺骗，性能突然崩塌
- 适合关注大模型自我改进机制的研究者
近期强化学习在训练大型推理模型中的成功，引发了关于自训练——即模型基于自身判断进行学习——是否能在强化学习中持续进行的疑问。本文通过多数投票这一简单自反馈机制开展系统实验，在合成与真实推理任务上均发现，该方法不仅能提升模型推理能力，还能生成更高质量的反馈以推动下一轮强化学习迭代。然而分析表明，长期使用自奖励机制会导致奖励欺骗，模型学会最大化训练（伪）奖励，引发性能的突然且彻底崩溃。这些结果凸显了反馈设计的核心挑战，呼吁未来研究探索实现持续自我改进的新机制。
原文摘要 · Abstract (English)
Recent successes of reinforcement learning (RL) in training large reasoning models motivate the question of whether self-training - the process where a model learns from its own judgments - can be sustained within RL. In this work, we study this question using majority voting as a simple self-feedback mechanism. On a comprehensive set of experiments on both synthetic and real reasoning tasks, we find that this basic approach improves not only the model's reasoning performance, but also its capability of generating better quality feedback for the next RL iteration, driving further model improvement. Yet our analysis also reveals a critical limitation of such a self-training paradigm - prolonged RL with self-reward leads to reward hacking where models learn to maximize training (pseudo-)reward, resulting in sudden and complete performance collapse. Together, these results highlight feedback design as the central challenge and call for future research on mechanisms to enable prolonged self-improvement.
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