A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
作者: Xiaoang Xu, Shuo Wang, Xu Han, Zhenghao Liu, Huijia Wu, Peipei Li, Zhiyuan Liu, Maosong Sun, Zhaofeng He
分类: cs.CL
发布日期: 2025-05-30 (更新: 2025-10-19)
备注: Accepted by NeurIPS 2025
🔗 代码/项目: GITHUB
💡 一句话要点
提出A*-Thought以解决低资源环境下推理效率问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 推理模型 树搜索 效率优化 双向估计 成本函数
📋 核心要点
- 现有方法在推理效率上存在不足，过长的思维链导致性能下降，无法有效利用资源。
- A-Thought通过将推理过程建模为搜索树，结合A搜索算法和成本函数，优化思维链的压缩。
- 实验结果显示，A*-Thought在低预算下提升QwQ-32B性能2.39倍，并在高预算下将输出令牌长度减少近50%。
📝 摘要（中文）
大型推理模型（LRMs）通过延长思维长度来实现卓越性能，但过长的思维轨迹降低了效率。现有方法往往假设过度思考，试图通过压缩思维链来提高推理效率，但这通常导致性能下降。为了解决这一问题，本文提出了A-Thought，一个基于高效树搜索的统一框架，旨在从这些模型产生的广泛推理链中识别和隔离最重要的思维。该方法将LRMs的推理过程形式化为搜索树，通过结合A搜索算法和特定于推理路径的成本函数，能够有效压缩思维链并确定高信息密度和低成本的推理路径。实验表明，A*-Thought在多个高级数学任务上有效平衡了性能和效率。
🔬 方法详解
问题定义：本文旨在解决大型推理模型在低资源环境下推理效率低下的问题。现有方法通常假设过度思考，导致推理链过长，影响性能和资源利用。
核心思路：A-Thought的核心思路是将推理过程视为一个搜索树，通过识别最重要的思维来压缩推理链，从而提高效率。结合A搜索算法和特定成本函数，使得推理路径既高效又信息密集。
技术框架：该方法的整体架构包括构建推理树、应用A*搜索算法、计算推理路径的成本，以及通过双向重要性估计机制优化搜索过程。主要模块包括推理树构建、路径评估和重要性估计。
关键创新：A*-Thought的主要创新在于引入了双向重要性估计机制，显著提高了搜索效率，超越了均匀采样的方法。这一设计使得模型能够在广泛的推理空间中更有效地找到重要思维。
关键设计：在参数设置上，A*-Thought使用了特定的成本函数来评估推理路径的有效性，并通过实验优化了搜索树的构建策略。损失函数设计上，强调了信息密度与计算成本的平衡。
🖼️ 关键图片
📊 实验亮点
实验结果表明，A*-Thought在低预算条件下能够将QwQ-32B的性能提升2.39倍，同时在高预算条件下将输出令牌长度减少近50%。这些结果展示了该方法在推理效率和性能之间的良好平衡。
🎯 应用场景
A*-Thought的研究成果在多个领域具有潜在应用价值，尤其是在需要高效推理的低资源环境中，如移动设备上的智能助手、边缘计算中的实时决策系统等。未来，该方法有望推动大型推理模型在实际应用中的普及与优化。
📄 摘要（原文）
Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing methods are stuck in the assumption of overthinking and attempt to reason efficiently by compressing the Chain-of-Thought, but this often leads to performance degradation. To address this problem, we introduce A-Thought, an efficient tree search-based unified framework designed to identify and isolate the most essential thoughts from the extensive reasoning chains produced by these models. It formulates the reasoning process of LRMs as a search tree, where each node represents a reasoning span in the giant reasoning space. By combining the A search algorithm with a cost function specific to the reasoning path, it can efficiently compress the chain of thought and determine a reasoning path with high information density and low cost. In addition, we also propose a bidirectional importance estimation mechanism, which further refines this search process and enhances its efficiency beyond uniform sampling. Extensive experiments on several advanced math tasks show that A-Thought effectively balances performance and efficiency over a huge search space. Specifically, A-Thought can improve the performance of QwQ-32B by 2.39$\times$ with low-budget and reduce the length of the output token by nearly 50% with high-budget. The proposed method is also compatible with several other LRMs, demonstrating its generalization capability. The code can be accessed at: https://github.com/AI9Stars/AStar-Thought.