Chain-of-thoughts (CoT) requires large language models (LLMs) to generate intermediate steps before reaching the final answer, and has been proven effective to help LLMs solve complex reasoning tasks. However, the inner mechanism of CoT still remains largely unclear. In this paper, we empirically study the role of CoT tokens in LLMs on two compositional tasks: multi-digit multiplication and dynamic programming. While CoT is essential for solving these problems, we find that preserving only tokens that store intermediate results would achieve comparable performance. Furthermore, we observe that storing intermediate results in an alternative latent form will not affect model performance. We also randomly intervene some values in CoT, and notice that subsequent CoT tokens and the final answer would change correspondingly. These findings suggest that CoT tokens may function like variables in computer programs but with potential drawbacks like unintended shortcuts and computational complexity limits between tokens. The code and data are available at https://github.com/solitaryzero/CoTs_are_Variables.

本研究探讨了思维链令牌（CoT）在大型语言模型（LLM）中如何影响复杂推理任务的解决，特别是在多位数字乘法和动态编程两项组合任务中。研究发现，仅保留存储中间结果的令牌便可实现类似的性能表现，同时探索了在不同形式下存储中间结果对模型性能的影响，表明CoT令牌在某种程度上类似于计算机程序变量，这为理解LLM的内部机制提供了新的视角。

思维链令牌作为计算机程序变量