用蚂蚁寻路原理优化大模型推理路径，提升复杂问题解决能力。
Pheromone-based Learning of Optimal Reasoning Paths
- 模拟蚂蚁觅食，让多模型协作探索最优推理路径。
- 在GSM8K等三任务上超越现有方法，准确率显著提升。
- 适合研究大模型推理优化与生物启发算法的学者。
大型语言模型（LLMs）通过思维链提示展现出强大的推理能力，但针对复杂问题发现有效推理方法仍具挑战，因中间步骤空间巨大。我们提出蚁群优化引导的思维树（ACO-ToT），将蚁群优化与LLMs结合，高效发现复杂问题的最优推理路径。受神经科学赫布学习启发，该方法使用多个独立微调的LLM'蚂蚁'在中央思维树中穿行并留下信息素痕迹，蚂蚁行动由现有信息素与自身专长加权决定。算法采用基于专家混合的评分函数评估完整推理路径，并通过信息素强化有效路径。在GSM8K、ARC-Challenge和MATH三个高难度推理任务上的实验表明，ACO-ToT显著优于现有思维链优化方法，说明将生物启发的集体搜索机制引入大模型推理可大幅提升其推理能力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through chain-of-thought prompting, yet discovering effective reasoning methods for complex problems remains challenging due to the vast space of possible intermediate steps. We introduce Ant Colony Optimization-guided Tree of Thought (ACO-ToT), a novel algorithm that combines ACO with LLMs to discover optimal reasoning paths for complex problems efficiently. Drawing inspiration from Hebbian learning in neurological systems, our method employs a collection of distinctly fine-tuned LLM "ants" to traverse and lay pheromone trails through a centralized tree of thought, with each ant's movement governed by a weighted combination of existing pheromone trails and its own specialized expertise. The algorithm evaluates complete reasoning paths using a mixture-of-experts-based scoring function, with pheromones reinforcing productive reasoning paths across iterations. Experiments on three challenging reasoning tasks (GSM8K, ARC-Challenge, and MATH) demonstrate that ACO-ToT performs significantly better than existing chain-of-thought optimization approaches, suggesting that incorporating biologically inspired collective search mechanisms into LLM inference can substantially enhance reasoning capabilities.
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