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今天的主题是：Evolving Deeper LLM Thinking

Summary

This paper introduces Mind Evolution, a novel evolutionary search strategy for enhancing the problem-solving capabilities of Large Language Models (LLMs) in natural language planning. The method uses an LLM to generate, combine, and refine potential solutions iteratively, guided by feedback from an evaluator. Mind Evolution outperforms existing inference strategies by effectively leveraging inference time compute without needing a formal problem definition. The paper showcases impressive results on benchmarks like TravelPlanner and Natural Plan, even introducing a new challenging task called StegPoet. The core innovation lies in its ability to optimize solutions directly in natural language space, eliminating the need for task formalization. Ablation studies confirm the importance of critical conversation and feedback mechanisms within the evolutionary process. The authors demonstrate that the approach can achieve high success rates, sometimes even exceeding 99%, and point to the potential for future development of LLM-based evaluators to broaden the scope of application.

本文介绍了Mind Evolution，这是一种新颖的进化搜索策略，旨在提升大型语言模型（LLMs）在自然语言规划中的问题解决能力。该方法利用LLM生成、组合和迭代优化潜在解决方案，并通过评估器的反馈指导进程。Mind Evolution通过有效利用推理时的计算资源，超越了现有的推理策略，且无需正式的问题定义。本文在TravelPlanner和Natural Plan等基准任务上展示了令人印象深刻的结果，并引入了一个名为StegPoet的新挑战任务。其核心创新在于能够直接在自然语言空间中优化解决方案，省去了任务形式化的需求。消融实验确认了在进化过程中关键对话和反馈机制的重要性。作者证明该方法能够实现高成功率，有时甚至超过99%，并指出未来开发基于LLM的评估器具有扩大应用范围的潜力。

原文链接：https://arxiv.org/abs/2501.09891