Self-Training Elicits Concise Reasoning in Large Language Models

Understand

Chain-of-thought (CoT) reasoning has enabled large language models (LLMs) to utilize additional computation through intermediate tokens to solve complex tasks.

• However, we posit that typical reasoning traces contain many redundant tokens, incurring extraneous inference costs.
• Upon examination of the output distribution of current LLMs, we find evidence on their latent ability to reason more concisely, relative to their default behavior.
• To elicit this capability, we propose simple fine-tuning methods which leverage self-generated concise reasoning paths obtained by best-of-N sampling and few-shot conditioning, in task-specific settings.