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ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback

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arxiv 2409.14826 v3 pith:4QRI7JAK submitted 2024-09-23 cs.CL cs.AI

classification cs.CLcs.AI
keywords instructionsllmsratetoolplanneradditionbetterfeedbackinstruction
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, tool-augmented LLMs have gained increasing attention. Given an instruction, tool-augmented LLMs can interact with various external tools in multiple rounds and provide a final answer. However, previous LLMs were trained on overly detailed instructions, which included API names or parameters, while real users would not explicitly mention these API details. This leads to a gap between trained LLMs and real-world scenarios. In addition, most works ignore whether the interaction process follows the instruction. To address these issues, we constructed a training dataset called MGToolBench, which contains statement and category-level instructions to better reflect real-world scenarios. In addition, we propose ToolPlanner, a two-stage reinforcement learning framework that utilizes path planning and two feedback mechanisms to enhance the LLM's task completion and instruction-following capabilities. Experimental results show that ToolPlanner significantly improves the Match Rate, Pass Rate and Win Rate by 26.8%, 20.2%, and 5.6% compared to the SOTA model. Human evaluation verifies that the multi-granularity instructions can better align with users' usage habits. Our data and code will be released upon acceptance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"

    cs.CL 2025-08 unverdicted novelty 7.0 of 10

    ToolGrad inverts tool-use dataset generation: build valid tool-call chains first, synthesize queries second, yielding lower cost and near-100% pass rates.

  2. Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A multi-agent LLM framework with reinforcement-learned tool use outperforms prior urban profiling models on GDP, population, and carbon estimation, including on unseen cities.

  3. CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

    cs.LG 2025-06 conditional novelty 5.0 of 10

    CheMatAgent uses hierarchical Monte Carlo tree search with separate policy and execution models, plus trained reward models, to improve tool selection and parameter filling on a new chemistry benchmark, ChemToolBench.

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