大模型的推理解释能跨模型通用吗？
Do explanations generalize across large reasoning models?
- 用一个模型的推理链让其他模型复现，测试解释是否通用
- 多数解释能提升不同模型间答案一致性，且与人类偏好相关
- 提出句级集成法提升一致性，适合需可靠推理的科研场景
大型推理模型（LRMs）在解题时会生成自然语言的思维链（CoT），可作为理解问题的可读解释。但这些解释是否具备泛化性——即是否反映问题本身的普遍规律而非模型特有模式——仍不明确。本文通过评估一种具体泛化性：一个模型的解释是否能让其他模型产生一致行为，来研究该问题。结果表明，思维链解释通常具备这种泛化能力，能显著提升不同模型间答案的一致性，且该一致性与人类偏好排名正相关，也与强化学习后训练有关。进一步分析发现，在特定条件下解释能带来一致输出，并提出一种简单的句子级集成策略以提升一致性。研究提示使用模型解释推导新知识需谨慎，并为刻画解释泛化性提供了框架。
原文摘要 · Abstract (English)
Large reasoning models (LRMs) produce a textual chain of thought (CoT) in the process of solving a problem, which serves as a potentially powerful tool to understand the problem by surfacing a human-readable, natural-language explanation. However, it is unclear whether these explanations generalize, i.e. whether they capture general patterns about the underlying problem rather than patterns which are esoteric to the LRM. This is a crucial question in understanding or discovering new concepts, e.g. in AI for science. We study this generalization question by evaluating a specific notion of generalizability: whether explanations produced by one LRM induce the same behavior when given to other LRMs. We find that CoT explanations often exhibit this form of generalization (i.e. they increase consistency between LRMs) and that this increased generalization is correlated with human preference rankings and post-training with reinforcement learning. We further analyze the conditions under which explanations yield consistent answers and propose a straightforward, sentence-level ensembling strategy that improves consistency. Taken together, these results prescribe caution when using LRM explanations to yield new insights and outline a framework for characterizing LRM explanation generalization.
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