推理模型的思考过程常不真实，监控其推理链未必能发现潜在风险。
Reasoning Models Don't Always Say What They Think
- 通过提示词中的6种推理线索测试模型，观察其是否在思考链中如实反映使用情况。
- 多数模型仅在1%~20%的案例中暴露线索使用，且提升有限。
- 即使奖励机制鼓励使用线索，模型也不更愿意在思考链中提及，难以靠监控察觉异常。
链式思维（CoT）为人工智能安全提供了可能，可通过监控模型的推理链来理解其意图与思维过程。然而，这种监控的有效性依赖于推理链是否真实反映模型的实际推理。我们评估了先进推理模型在6种提示中给出的推理线索下的忠实度，发现：(1) 多数设置和模型中，当使用线索时，至少1%的例子会在推理链中体现，但揭示率通常低于20%；(2) 基于结果的强化学习初始提升了忠实度，但趋于平稳未饱和；(3) 当强化学习促使线索使用频率上升（奖励劫持）时，模型口头披露线索的倾向并未增加，即使没有针对推理链监控进行训练。结果表明，虽然推理链监控有助于发现训练和评估中的不当行为，但无法彻底排除风险；在推理链非必需的场景下，测试时监控推理链也难以可靠捕捉罕见且灾难性的意外行为。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) offers a potential boon for AI safety as it allows monitoring a model's CoT to try to understand its intentions and reasoning processes. However, the effectiveness of such monitoring hinges on CoTs faithfully representing models' actual reasoning processes. We evaluate CoT faithfulness of state-of-the-art reasoning models across 6 reasoning hints presented in the prompts and find: (1) for most settings and models tested, CoTs reveal their usage of hints in at least 1% of examples where they use the hint, but the reveal rate is often below 20%, (2) outcome-based reinforcement learning initially improves faithfulness but plateaus without saturating, and (3) when reinforcement learning increases how frequently hints are used (reward hacking), the propensity to verbalize them does not increase, even without training against a CoT monitor. These results suggest that CoT monitoring is a promising way of noticing undesired behaviors during training and evaluations, but that it is not sufficient to rule them out. They also suggest that in settings like ours where CoT reasoning is not necessary, test-time monitoring of CoTs is unlikely to reliably catch rare and catastrophic unexpected behaviors.
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