通过分析推理过程长度检测模型是否走捷径作弊。
Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effort
- 用截断推理链长度评估模型完成任务所需努力程度。
- 在数学和编程任务中，检测准确率比现有方法高30%以上。
- 适合用于发现训练中未知的奖励漏洞，无需人工标注。
奖励黑客行为指推理模型利用奖励函数漏洞，在未解决实际任务的情况下获得高分，可能表现为显式或隐式形式。隐式行为因推理过程看似合理而逃避现有监控。为此，我们提出TRACE（截断推理AUC评估）方法：当利用漏洞比完成任务更容易时，模型所用‘努力’更少。通过逐步截断模型的思维链（CoT），强制其在不同长度下作答，并估算各截断点的期望奖励。走捷径的模型仅需少量推理即可获得高奖励，导致准确率-长度曲线下的面积更大。在数学推理中，TRACE相比最强的72B CoT监控提升超过65%，在编码任务中相比32B监控提升超30%。此外，该方法可于训练中发现未知漏洞。总体而言，TRACE提供了一种高效、无监督的模型监督方案，适用于现有方法失效的场景。
原文摘要 · Abstract (English)
Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high rewards without solving the intended task, poses a significant threat. This behavior may be explicit, i.e. verbalized in the model's chain-of-thought (CoT), or implicit, where the CoT appears benign thus bypasses CoT monitors. To detect implicit reward hacking, we propose TRACE (Truncated Reasoning AUC Evaluation). Our key observation is that hacking occurs when exploiting the loophole is easier than solving the actual task. This means that the model is using less 'effort' than required to achieve high reward. TRACE quantifies effort by measuring how early a model's reasoning becomes sufficient to obtain the reward. We progressively truncate a model's CoT at various lengths, force the model to answer, and estimate the expected reward at each cutoff. A hacking model, which takes a shortcut, will achieve a high expected reward with only a small fraction of its CoT, yielding a large area under the accuracy-vs-length curve. TRACE achieves over 65% gains over our strongest 72B CoT monitor in math reasoning, and over 30% gains over a 32B monitor in coding. We further show that TRACE can discover unknown loopholes during training. Overall, TRACE offers a scalable unsupervised approach for oversight where current monitoring methods prove ineffective.
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