状态优于标记：推理标记的角色定位

摘要

大型语言模型（LLMs）在生成最终答案前会产出推理标记以提升复杂任务的表现。虽然这些标记序列看似人类思维过程，但实证研究表明它们并不能真实反映模型的实际推理机制。为弥合这种表象与功能之间的鸿沟，我们提出"标记状态"（SoT）概念框架。该框架将推理标记重新定义为外部化的计算状态——而非语言叙述，它是模型无状态生成周期中唯一持续存在的信息载体。这解释了为何这些标记在推动正确推理的同时，却无法作为可信的文本解释被阅读，并揭示了此前被忽视的关于推理标记的研究课题。我们认为，要真正理解LLMs的运作机制，研究必须超越将推理标记作为文本来解读的层面，转而聚焦于将其作为状态信息进行解码。

English

Large Language Models (LLMs) can generate reasoning tokens before their final answer to boost performance on complex tasks. While these sequences seem like human thought processes, empirical evidence reveals that they are not a faithful explanation of the model's actual reasoning process. To address this gap between appearance and function, we introduce the State over Tokens (SoT) conceptual framework. SoT reframes reasoning tokens not as a linguistic narrative, but as an externalized computational state -- the sole persistent information carrier across the model's stateless generation cycles. This explains how the tokens can drive correct reasoning without being a faithful explanation when read as text and surfaces previously overlooked research questions on these tokens. We argue that to truly understand the process that LLMs do, research must move beyond reading the reasoning tokens as text and focus on decoding them as state.

状态优于标记：推理标记的角色定位

State over Tokens: Characterizing the Role of Reasoning Tokens

摘要

Support