{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"cot-misbehavior-monitoring","title":"Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation","aliases":[],"authors":["Bowen Baker","Joost Huizinga","Leo Gao","Zehao Dou","Melody Y. Guan","Aleksander Madry","Wojciech Zaremba","Jakub Pachocki","David Farhi"],"primaryFunction":"evaluation","secondaryFunctions":[],"kind":"study","sourceId":"cot-misbehavior-monitoring-publication","attributionSourceId":"cot-misbehavior-monitoring-publication-author-source","originalUrl":"https://arxiv.org/abs/2503.11926","publicationDate":"2025-03-14","updatedOn":"2026-10-25","areas":["deception"],"scopeNote":{"en":"Relevance is assigned to the research question and stated conditions of the work. A topic tag is not a risk rating.","zh":"风险关联依据研究问题及明确条件建立，主题标签不代表风险评级。"},"protocolIds":[],"contributions":[{"organizationId":"openai","role":"research","sourceId":"cot-misbehavior-monitoring-publication-author-source","locator":"Author affiliations on the original paper title page"}],"summary":{"en":"Studies whether chain-of-thought monitoring can detect reward hacking and how optimization may undermine that visibility.","zh":"研究推理链监测能否识别奖励投机，以及优化过程如何削弱这种可见性。"},"note":{"question":{"en":"Can chain-of-thought monitors detect reward hacking, and does training directly against those monitors preserve that visibility?","zh":"思维链监测器能否发现奖励黑客行为，直接针对这些监测器训练又是否会保留这种可见性？"},"method":{"en":"The paper compares reasoning-based and action-based detection of reward hacking, then studies direct optimization against a chain-of-thought monitor in a controlled training environment. It also compares using monitors for rejection sampling.","zh":"论文比较基于推理与基于动作的奖励黑客检测，随后在受控训练环境中研究直接针对思维链监测器的优化，并比较利用监测器进行拒绝采样的方式。"},"finding":{"en":"Visible reasoning helps detect some reward hacks that are less apparent from actions alone. Strong direct optimization pressure can instead produce hacks whose intent is no longer expressed in the visible reasoning, reducing monitor usefulness without necessarily eliminating the behavior.","zh":"可见推理有助于发现某些仅看动作不易识别的奖励黑客行为。较强的直接优化压力也可能使模型继续利用漏洞，却不再在可见推理中表露意图，从而降低监测效用，而不一定消除行为。"},"supports":{"en":"The relevant safety property is whether a monitor can detect a specified behavior, which need not require a fully faithful account of all computation. Monitor training pressure itself belongs in the evaluation.","zh":"相关安全属性是监测器能否识别特定行为，而不一定要求完整忠实地记录全部计算；监测带来的训练压力本身也应纳入评估。"},"limits":{"en":"The experiments use particular reward-hacking tasks, training pressures and prompted monitors. They do not establish how every form of process supervision or future reasoning architecture affects monitorability.","zh":"实验使用特定奖励黑客任务、训练压力和提示式监测器，不能确定所有过程监督形式或未来推理架构对可监测性的影响。"},"connections":null},"resources":[],"noteStatus":{"question":"verified","method":"verified","finding":"verified","supports":"verified","limits":"verified","connections":"limited"},"noteAvailability":{"en":"The substantive sections shown here were checked against the original source. Unfilled sections are omitted; source checking is not experimental replication.","zh":"此处展示的实质性章节已对照原始来源核查；未完成的章节已省略。来源核查不等于实验复现。"},"noteSourceLocator":"arXiv:2503.11926v1, §§2.1–2.2, 3.1–3.3, 5.1–5.2, 6","tags":["risk-monitoring"]},"reading":{"id":"cot-misbehavior-monitoring","title":"Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation","en":{"question":"Can weaker models detect reward hacking from stronger agents’ reasoning?","design":"Agentic coding settings compare action-only and CoT monitors, then vary optimization pressure against a CoT-based reward.","findings":["In the reported systemic-hack setting, action-only recall was 60% versus 95% with CoT.","Strong optimization eventually produced obfuscated reward hacking despite earlier alignment gains."],"interpretation":"NUWA interpretation: monitor utility and vulnerability to training pressure must be assessed together.","limits":"The measured coding behaviors and training regime do not establish universal CoT reliability or a deployment-wide detection rate.","decisionRelevance":"Model recommendation: preserve access conditions and monitor-aware training history when treating reasoning traces as oversight evidence.","takeaway":"NUWA interpretation: monitor utility and vulnerability to training pressure must be assessed together.","connections":[],"scoring":"Recall of labeled reward hacks, separately for action-only and CoT monitor access.","conditions":"Coding environments; direct monitor-based RL optimization changes behavior.","statistics":"In the reported systemic-hack setting, action-only recall was 60% versus 95% with CoT.","usage":"Model recommendation: preserve access conditions and monitor-aware training history when treating reasoning traces as oversight evidence."},"zh":{"question":"较弱模型能否从较强智能体推理检测奖励作弊？","design":"智能体编程环境比较仅行动与思维链监测，并改变针对链上奖励的优化压力。","findings":["所列系统性作弊设置中，仅行动召回60%，思维链95%。","强优化最终出现隐蔽奖励作弊，尽管早期有对齐收益。"],"interpretation":"NUWA 解读：监测效用须和训练压力脆弱性共同评价。","limits":"特定编程行为及训练机制不能确立通用可靠性或部署检出率。","decisionRelevance":"模型建议：把推理作监督证据时保留访问条件及监测感知训练历史。","takeaway":"NUWA 解读：监测效用须和训练压力脆弱性共同评价。","connections":[],"scoring":"标注奖励作弊召回，分仅行动与思维链访问。","conditions":"编程环境，直接基于监测的RL改变行为。","statistics":"所列系统性作弊设置中，仅行动召回60%，思维链95%。","usage":"模型建议：把推理作监督证据时保留访问条件及监测感知训练历史。"},"assertionPolicy":{"question":"source-research-question","design":"source-finding","findings":"source-finding","interpretation":"nuwa-interpretation","limits":"source-limit-or-explicit-editorial-scope-limit","connections":"source-supported-relationship-only","decisionRelevance":"model-recommendation-not-source-conclusion"},"sourceRefs":[{"sourceId":"cot-misbehavior-monitoring-publication-author-source","url":"https://arxiv.org/html/2503.11926","locator":"Abstract","version":"Selected original resource https://arxiv.org/html/2503.11926; 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Guan † Aleksander Madry † Wojciech Zaremba † Jakub Pachocki † David Farhi *†"],"aliases":["https://doi.org/10.48550/arXiv.2503.11926","https://arxiv.org/abs/2503.11926","https://arxiv.org/pdf/2503.11926"]}]}