Rational Metareasoning for Large Language Models

• URL: http://arxiv.org/abs/2410.05563v1
• Date: Mon, 7 Oct 2024 23:48:52 GMT
• Title: Rational Metareasoning for Large Language Models
• Authors: C. Nicolò De Sabbata, Theodore R. Sumers, Thomas L. Griffiths,
• Abstract summary: Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs) This work introduces a novel approach based on computational models of metareasoning used in cognitive science. We develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning.
• Score: 5.5539136805232205
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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