  Chain of Thought (CoT) is significant in improving the reasoning abilities of
large language models (LLMs). However, the correlation between the
effectiveness of CoT and the length of reasoning steps in prompts remains
largely unknown. To shed light on this, we have conducted several empirical
experiments to explore the relations. Specifically, we design experiments that
expand and compress the rationale reasoning steps within CoT demonstrations
while keeping all other factors constant. We have the following key findings.
First, the results indicate that lengthening the reasoning steps in prompts,
even without adding new information into the prompt, considerably enhances
LLMs' reasoning abilities across multiple datasets. Alternatively, shortening
the reasoning steps, even while preserving the key information, significantly
diminishes the reasoning abilities of models. This finding highlights the
importance of the number of steps in CoT prompts and provides practical
guidance to make better use of LLMs' potential in complex problem-solving
scenarios. Second, we also investigated the relationship between the
performance of CoT and the rationales used in demonstrations. Surprisingly, the
result shows that even incorrect rationales can yield favorable outcomes if
they maintain the requisite length of inference. Third, we observed that the
advantages of increasing reasoning steps are task-dependent: simpler tasks
require fewer steps, whereas complex tasks gain significantly from longer
inference sequences. The code is available at
https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models
