HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking

• URL: http://arxiv.org/abs/2505.02322v2
• Date: Thu, 29 May 2025 03:57:01 GMT
• Title: HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
• Authors: Runquan Gui, Zhihai Wang, Jie Wang, Chi Ma, Huiling Zhen, Mingxuan Yuan, Jianye Hao, Defu Lian, Enhong Chen, Feng Wu,
• Abstract summary: We propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.<n> Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.
• Score: 109.09735490692202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning. However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks. To address these challenges, we propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning. The hypertree structure enables LLMs to engage in hierarchical thinking by flexibly employing the divide-and-conquer strategy, effectively breaking down intricate reasoning steps, accommodating diverse constraints, and managing multiple distinct sub-tasks in a well-organized manner. We further introduce an autonomous planning framework that completes the planning process by iteratively refining and expanding the hypertree-structured planning outlines. Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.

Related papers

• ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning [30.030923956489385]
  Vision-language-action (VLA) reasoning tasks require agents to interpret multimodal instructions, perform long-horizon planning, and act adaptively in dynamic environments.<n>Existing approaches typically train VLA models in an end-to-end fashion, directly mapping inputs to actions without explicit reasoning.<n>We propose ThinkAct, a dual-system framework that bridges high-level reasoning with low-level action execution via reinforced visual latent planning.
  arXiv  Detail & Related papers  (2025-07-22T17:59:46Z)
• PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving [66.42260489147617]
  We introduce PLAN-TUNING, a framework that distills synthetic task decompositions from large-scale language models.<n>Plan-TUNING fine-tunes smaller models via supervised and reinforcement-learning objectives to improve complex reasoning.<n>Our analysis demonstrates how planning trajectories improves complex reasoning capabilities.
  arXiv  Detail & Related papers  (2025-07-10T07:30:44Z)
• Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints [31.631832677979826]
  We propose a novel parallel planning paradigm, which Decomposes, Plans for subtasks in Parallel, and Merges subplans into a final plan (DPPM)<n>Specifically, DPPM decomposes the complex task based on constraints into subtasks, generates the subplan for each subtask in parallel, and merges them into a global plan.<n> Experimental results demonstrate that DPPM significantly outperforms existing methods in travel planning tasks.
  arXiv  Detail & Related papers  (2025-06-03T09:33:13Z)
• Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification [5.727096041675994]
  Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks.<n>We propose a neuro-symbolic approach that enhances LLMs-based planners with Knowledge Graph-based RAG for hierarchical plan generation.
  arXiv  Detail & Related papers  (2025-04-06T18:36:30Z)
• Vote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning Pipeline via Voting [4.500734889060007]
  This paper addresses the synergy between large language models (LLMs) and task planning systems.<n>We propose Vote-Tree-Planner to minimize redundancy while enhancing planning effectiveness.
  arXiv  Detail & Related papers  (2025-02-13T20:08:06Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation [8.180994118420053]
  Nl2Hltl2Plan is a framework that translates natural language commands into hierarchical Linear Temporal Logic (LTL)<n>First, an LLM transforms instructions into a Hierarchical Task Tree, capturing logical and temporal relations.<n>Next, a fine-tuned LLM converts sub-tasks into flat formulas, which are aggregated into hierarchical specifications.
  arXiv  Detail & Related papers  (2024-08-15T14:46:13Z)
• A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models [15.874604623294427]
  Multi-Phases planning problem involves multiple interconnected stages, such as outlining, information gathering, and planning. Existing reasoning approaches have struggled to effectively address this complex task. Our research aims to address this challenge by developing a human-like planning framework for LLM agents.
  arXiv  Detail & Related papers  (2024-05-28T14:13:32Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• Guiding Language Model Reasoning with Planning Tokens [122.43639723387516]
  Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks. We propose a hierarchical generation scheme to encourage a more structural generation of chain-of-thought steps. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme.
  arXiv  Detail & Related papers  (2023-10-09T13:29:37Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.