Non-myopic Generation of Language Models for Reasoning and Planning

• URL: http://arxiv.org/abs/2410.17195v3
• Date: Mon, 28 Oct 2024 17:28:51 GMT
• Title: Non-myopic Generation of Language Models for Reasoning and Planning
• Authors: Chang Ma, Haiteng Zhao, Junlei Zhang, Junxian He, Lingpeng Kong,
• Abstract summary: This paper proposes a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. Our experiments show significant improvements in a wide range of tasks for math, coding, and agents.
• Score: 45.75146679449453
• License:
• Abstract: Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. Despite their success in various domains like mathematical problem-solving and coding, LLMs face challenges in ensuring reliable and optimal planning due to their inherent myopic nature of autoregressive decoding. This paper revisits LLM reasoning from an optimal-control perspective, proposing a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. By re-weighting LLM distributions based on foresight trajectories, Predictive-Decoding aims to mitigate early errors and promote non-myopic planning. Our experiments show significant improvements in a wide range of tasks for math, coding, and agents. Furthermore, Predictive-Decoding demonstrates computational efficiency, outperforming search baselines with reduced computational resources. This study provides insights into optimizing LLM planning capabilities.

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