AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

• URL: http://arxiv.org/abs/2512.13278v1
• Date: Mon, 15 Dec 2025 12:38:04 GMT
• Title: AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
• Authors: Jiaru Zou, Ling Yang, Yunzhe Qi, Sirui Chen, Mengting Ai, Ke Shen, Jingrui He, Mengdi Wang,
• Abstract summary: Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories.<n>Existing approaches assume a fixed inventory of tools, limiting LLM agents' adaptability to new or evolving toolsets.<n>We present AutoTool, a framework that equips LLM agents with dynamic tool-selection capabilities throughout their reasoning trajectories.
• Score: 79.65732142949014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories while interleaving external tool use. Existing approaches assume a fixed inventory of tools, limiting LLM agents' adaptability to new or evolving toolsets. We present AutoTool, a framework that equips LLM agents with dynamic tool-selection capabilities throughout their reasoning trajectories. We first construct a 200k dataset with explicit tool-selection rationales across 1,000+ tools and 100+ tasks spanning mathematics, science, code generation, and multimodal reasoning. Building on this data foundation, AutoTool employs a dual-phase optimization pipeline: (i) supervised and RL-based trajectory stabilization for coherent reasoning, and (ii) KL-regularized Plackett-Luce ranking to refine consistent multi-step tool selection. Across ten diverse benchmarks, we train two base models, Qwen3-8B and Qwen2.5-VL-7B, with AutoTool. With fewer parameters, AutoTool consistently outperforms advanced LLM agents and tool-integration methods, yielding average gains of 6.4% in math & science reasoning, 4.5% in search-based QA, 7.7% in code generation, and 6.9% in multimodal understanding. In addition, AutoTool exhibits stronger generalization by dynamically leveraging unseen tools from evolving toolsets during inference.

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