用分层分解提升大模型空间推理能力，让其更会规划路径和解谜。
Deconstructing Spatial Complexity: Hierarchical Decomposition for LLM Spatial Reasoning
- 通过识别关键中间状态，将复杂空间任务拆解为可管理子任务
- 在导航、规划和策略游戏上达到当前最优表现，显著超越基线
- 结合蒙特卡洛树搜索与模型不确定性，优化路径规划决策
大语言模型在通用语言理解与推理方面表现出色，但在空间推理任务中持续表现不佳，严重限制了其在具身智能等场景的应用。受分层强化学习成功的启发，本文提出一种面向大模型空间推理的分层任务分解新方法：引导模型通过识别关键中间状态并生成简化子环境，将复杂任务分解为可管理的子任务。然而，研究发现大模型因缺乏足够的空间先验知识，常无法推导出最优中间状态，导致任务分解效果不佳。为此，提出基于蒙特卡洛树搜索的组相对策略优化方法（M-GRPO），通过融合模型的先验预测概率与认知不确定性，重构UCT公式，并引入更细粒度的优势函数，使模型能学习到更优路径规划策略。实验表明，该方法在导航、规划及策略类任务上均显著提升性能，达到当前最佳水平，为大模型在真实世界应用开辟了新路径。
原文摘要 · Abstract (English)
LLMs have shown remarkable proficiency in general language understanding and reasoning. However, they consistently underperform in spatial reasoning that severely limits their application, particularly in embodied intelligence. Inspired by the success of hierarchical reinforcement learning, this paper introduces a novel method for hierarchical task decomposition in LLM spatial reasoning. Our approach guides LLMs to decompose complex tasks into manageable sub-tasks by identifying key intermediate states and generating simplified sub-environments. However, we identify that LLMs often fail to derive optimal intermediate states due to their insufficient spatial prior, leading to sub-optimal task decomposition. To address this limitation and enhance its planning capability, we propose the MCTS-Guided Group Relative Policy Optimization (M-GRPO), where we reformulate the UCT formula by incorporating the LLM's prior predictive probabilities alongside its epistemic uncertainty. Furthermore, we implement a more fine-grained advantage function, enabling the model to learn optimal path planning. Experimental results demonstrate that our method substantially improves LLM performance on spatial tasks, including navigation, planning, and strategic games, achieving state-of-the-art results. This work paves the way for LLMs in real-world applications.
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