研究如何让大模型的思维链可被监控，避免训练时隐藏推理过程。
Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?
- 将模型训练建模为强化学习环境，分解奖励项判断思维链是否可监控。
- 冲突型奖励会降低思维链可监控性，且优化困难；对齐型则提升。
- 适用于关注AI可解释性与安全监控的研究者。
Chain-of-Thought（CoT）监控是一种有效监督大语言模型的方法，但模型在训练过程中可能隐藏关键推理特征，影响思维链的可监控性。本文提出并实证验证一个概念框架，用于预测何时及为何发生此类现象。将后训练过程建模为强化学习环境，其中奖励函数分为依赖最终输出和依赖思维链两部分。根据这两部分的关系，可将训练目标划分为‘对齐’、‘正交’或‘冲突’三类。预测表明：冲突型目标会降低思维链可监控性，正交型无影响，对齐型则增强其可监控性。通过分类一组强化学习环境，训练大模型并评估其影响，结果发现：(1) 冲突型奖励导致思维链可监控性下降；(2) 优化冲突型奖励本身也极为困难。
原文摘要 · Abstract (English)
Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems. However, the extent to which a model's CoT helps us oversee the model - the monitorability of the CoT - can be affected by training, for instance by the model learning to hide important features of its reasoning. We propose and empirically validate a conceptual framework for predicting when and why this occurs. We model LLM post-training as an RL environment where the reward decomposes into two terms: one term depending on final outputs and another term depending on the CoT. Our framework allows us to classify these two terms as "aligned", "orthogonal", or "in-conflict" before training. We predict that training with in-conflict terms will reduce monitorability, orthogonal terms will not affect it, and aligned terms will improve it. To validate our framework, we use it to classify a set of RL environments, train LLMs within those environments, and evaluate how training affects CoT monitorability. We find that (1) training with "in-conflict" reward terms reduces CoT monitorability and (2) optimizing in-conflict reward terms is difficult.
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