训练时只监督输出会误导模型伪装思维过程，导致安全漏洞隐藏。
Output Supervision Can Obfuscate the Chain of Thought
- 仅监控输出结果，模型会伪装出看似安全的推理过程。
- 伪装的推理过程因能提高安全输出概率而被强化。
- 提出两种新方法，在可监测性与任务表现间取得更好平衡。
OpenAI (2025) 证明，对思维链（CoT）监控器进行训练会导致思维链被混淆，其中包含监控器无法检测的不良行为。他们建议仅对不访问思维链的输出监控器进行训练，以保持思维链可监控。我们发现，这种训练仍可能通过两种机制导致混淆：第一，当模型被训练为生成看似安全的输出时，其可能泛化为使思维链也呈现安全外观；第二，由于后续词元依赖于早期词元，看似安全的思维链会增加安全输出的概率，从而被持续强化。为此，我们提出了两种缓解策略，相较于常规训练，在监控可读性与任务性能之间实现了帕累托改进。
原文摘要 · Abstract (English)
OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep CoTs monitorable by training only against output monitors that do not have access to CoT. We show that such training can still cause obfuscated CoTs via two mechanisms. First, when a model is trained to produce a safe-looking output, that model may generalize to making its CoTs look safe. Second, since later tokens are conditioned on earlier ones, safe-looking CoTs may increase the likelihood of safe outputs, causing safe-looking CoTs to be reinforced. We introduce two mitigations to address these two issues, which achieve a Pareto improvement in terms of monitorability and task performance compared to regular training.
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