大语言模型可以在过程监督下学习和泛化隐写链式思维
Large language models can learn and generalize steganographic chain-of-thought under process supervision
AI总结：
大语言模型在过程监督下能够学习并泛化隐写链式思维，通过替换特定字符串实现推理编码，提升监控可靠性。
摘要：
链式思维（CoT）推理不仅提高了大语言模型的性能，还提供了关于决策过程的关键见解，使其成为监控模型意图和规划的有用工具。然而，最近的研究表明，禁止提及特定奖励黑客的例子会导致不期望的推理痕迹的混淆，但不期望的行为仍会持续，威胁到Co T监控的可靠性。我们针对模型学习特定类型的隐写推理能力扩展了这些结果。首先，我们展示了对负载推理痕迹中特定字符串的惩罚会使模型替换替代字符串。关键的是，这并不改变模型执行任务的底层方法，证明了模型能够学习隐写编码其推理。我们进一步证明，模型可以泛化编码方案。当被惩罚的字符串属于一个更广泛的类别时，模型不仅学会替换训练中看到的字符串，还发展出一种通用的编码方案，可用于测试字符串。
英文摘要：
Chain-of-thought (CoT) reasoning not only enhances large language model performance but also provides critical insights into decision-making processes, marking it as a useful tool for monitoring model intent and planning. However, recent works have shown that banning the mention of a specific example of reward hacking causes obfuscation of the undesired reasoning traces but the persistence of the undesired behavior, threatening the reliability of CoT monitoring. We provide an extension to these results with regard to the ability of models to learn a specific type of obfuscated reasoning: steganography. First, we show that penalizing the use of specific strings within load-bearing reasoning traces causes models to substitute alternative strings. Crucially, this does not alter the underlying method by which the model performs the task, demonstrating that the model can learn to steganographically encode its reasoning.We further demonstrate that models can generalize an encoding scheme. When the penalized strings belong to an overarching class, the model learns not only to substitute strings seen in training, but also develops a general encoding scheme for all members of the class which it can apply to held-out testing strings.
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