通过约束梯度方向防止语言模型偷懒，提升任务真实表现
Directional Alignment Mitigates Reward Hacking in Reinforcement Learning for Language Models
- 用参数更新的主方向分析优化轨迹，识别偷懒行为
- 新方法使模型延迟使用捷径，数学推理任务性能更稳
- 适合研究强化学习中奖励欺骗问题的学者
奖励黑客现象指模型通过捷径提升代理奖励而非完成真实任务。我们从语言模型强化学习更新的几何特性出发，发现偷懒源于优化偏离稳定低维轨迹。通过分析参数更新的主奇异方向，发现奖励黑客运行的定向变化显著大于正常运行。基于此，提出可信方向投影，将梯度约束在干净参考子空间内。在数学推理任务的奖励黑客实验中，该方法延缓了捷径利用，更好保持了任务性能。
原文摘要 · Abstract (English)
Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinforcement learning updates in language models and argue that hacking emerges when optimization drifts away from a stable low-dimensional learning trajectory. We analyze this drift through dominant singular directions of parameter updates and show that reward-hacking runs exhibit substantially larger directional change than clean runs. Motivated by this observation, we introduce trusted-direction projection, which constrains gradients to remain within a clean reference subspace. Across reward-hacking experiments on mathematical reasoning, the proposed approach delays shortcut exploitation and better preserves task performance.
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