Rational Metareasoning for Large Language Models
Keywords: LLM, Metareasoning, Problem solving, Chain of Thought, Inference Optimization, Value of Computation
TL;DR: This paper presents a method inspired by cognitive science to iteratively train LLMs to optimize reasoning processes, significantly cutting costs without sacrificing performance.
Abstract: Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs), deploying additional inference-time compute to improve task performance. However, as LLMs increase in both size and adoption, inference costs are correspondingly becoming increasingly burdensome. How, then, might we optimize reasoning's cost-performance tradeoff? This work introduces a novel approach based on computational models of metareasoning used in cognitive science, training LLMs to selectively use intermediate reasoning steps only when necessary. We first develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning, then use this reward function with Expert Iteration to train the LLM. Compared to few-shot chain-of-thought prompting and STaR, our method significantly reduces inference costs (20-37\% fewer tokens generated across three models) while maintaining task performance across diverse datasets.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 11955
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