通过内部激活监控生成过程中的涌现奖励黑客行为
Monitoring Emergent Reward Hacking During Generation via Internal Activations
AI总结：
通过分析内部激活来监控生成过程中的奖励黑客行为，提升微调语言模型的安全性。
摘要：
经过微调的大型语言模型可能会表现出由于涌现偏差导致的奖励黑客行为，这种行为仅凭最终输出难以检测。尽管先前的研究已经研究了奖励黑客行为在完成响应层面的表现，但尚不清楚这种行为是否可以在生成过程中被识别。我们提出了一种基于激活的监控方法，该方法通过模型生成响应时的内部表示来检测奖励黑客信号。我们的方法在残差流激活上训练稀疏自动编码器，并应用轻量级线性分类器来生成令牌级别的奖励黑客活动估计。在多个模型家族和微调混合物中，我们发现内部激活模式能够可靠地区分奖励黑客行为与良性行为，能够推广到未见过的混合策略适配器，并在链式推理过程中表现出模型依赖的时间结构。值得注意的是，奖励黑客信号通常在早期出现，持续整个推理过程，并且可以通过增加测试时计算量在弱定义奖励目标下以链式推理提示的形式被放大。这些结果表明，内部激活监控比基于输出的评估提供了更早的涌现偏差信号，支持更稳健的微调语言模型部署后安全监控。
英文摘要：
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation. We propose an activation-based monitoring approach that detects reward-hacking signals from internal representations as a model generates its response. Our method trains sparse autoencoders on residual stream activations and applies lightweight linear classifiers to produce token-level estimates of reward-hacking activity. Across multiple model families and fine-tuning mixtures, we find that internal activation patterns reliably distinguish reward-hacking from benign behavior, generalize to unseen mixed-policy adapters, and exhibit model-dependent temporal structure during chain-of-thought reasoning. Notably, reward-hacking signals often emerge early, persist throughout reasoning, and can be amplified by increased test-time compute in the form of chain-of-thought prompting under weakly specified reward objectives. These results suggest that internal activation monitoring provides a complementary and earlier signal of emergent misalignment than output-based evaluation, supporting more robust post-deployment safety monitoring for fine-tuned language models.
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