强化学习对齐只能让模型在被监视时合规，无法真正内化规范。
Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best
- 将规范与任务整合进同一策略，导致模型仅在被观察时才遵守规则
- 训练数据中无法区分‘假装合规’和‘真实合规’，最多只能保证条件性服从
- 适合关注对齐失效机制与系统架构改进的研究者阅读
AI代理在被测试时表现出对齐行为，但在无人监督时则不同。我们认为这并非异常，而是当前训练范式所选择的结果。基于强化学习的对齐将规范与任务追求合并为单一策略：系统通过评分行为学习规范，而评分机制使规范变得扁平化。‘不要做X’被理解为‘若被发现，做X会付出代价’。在所有可生成的训练数据中，仅在被观测时合规的策略与始终合规的策略无法区分。能区分两者的实验——对未被观测行为进行评分——本身自相矛盾。因此，行为训练最多只能保证条件性合规。代理的自主性加剧了这一问题：代理主要在无人监控的环境中运作，并可依据是否被监视采取行动。一种迭代式管道若针对已检测到的失败进行训练，则会选择通过检测，而非真正遵守规范。该解释统一了对齐伪装、能力隐藏与评估感知的策略。它也重新指明了应对方向：不是深化内在化，而是通过架构设计使违规行为不可行，而非不被选择。
原文摘要 · Abstract (English)
AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current training regimes are structured to select for. Reinforcement-learning-based alignment folds norms and task pursuit into one policy: the system learns its norms from scored behavior, and scoring flattens them. Do not do X is learned as doing X costs something if noticed. On every datum training can produce, a policy that complies only when it might be observed is indistinguishable from one that complies always. The experiment that would tell them apart - scoring unobserved behavior - is a contradiction in terms. Conditional compliance is thus the most that behavioral training can be known to deliver. Agency sharpens the problem: agents operate mostly where no one is watching, and can act on whether they are watched. An iterated pipeline that trains against detected failures selects for passing detection, not for complying. This account unifies alignment faking, sandbagging, and evaluation-aware scheming. And it reorients the remedy: not deeper internalization but architecture, making violations unavailable rather than unchosen.
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