Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use

• URL: http://arxiv.org/abs/2509.12867v1
• Date: Tue, 16 Sep 2025 09:22:21 GMT
• Title: Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use
• Authors: Yabo Zhang, Yihan Zeng, Qingyun Li, Zhen Hu, Kavin Han, Wangmeng Zuo,
• Abstract summary: Large language models (LLMs) have demonstrated strong capabilities in language understanding and reasoning.<n>We propose Tool-R1, a reinforcement learning framework that enables LLMs to perform general, compositional, and multi-step tool use.
• Score: 50.02614257515131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong capabilities in language understanding and reasoning, yet they remain limited when tackling real-world tasks that require up-to-date knowledge, precise operations, or specialized tool use. To address this, we propose Tool-R1, a reinforcement learning framework that enables LLMs to perform general, compositional, and multi-step tool use by generating executable Python code. Tool-R1 supports integration of user-defined tools and standard libraries, with variable sharing across steps to construct coherent workflows. An outcome-based reward function, combining LLM-based answer judgment and code execution success, guides policy optimization. To improve training efficiency, we maintain a dynamic sample queue to cache and reuse high-quality trajectories, reducing the overhead of costly online sampling. Experiments on the GAIA benchmark show that Tool-R1 substantially improves both accuracy and robustness, achieving about 10\% gain over strong baselines, with larger improvements on complex multi-step tasks. These results highlight the potential of Tool-R1 for enabling reliable and efficient tool-augmented reasoning in real-world applications. Our code will be available at https://github.com/YBYBZhang/Tool-R1.

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