ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
In this work, we embrace the RL paradigm and introduce ReTool, a Tool-augmented Reinforcement learning framework explicitly designed to guide LLMs towards optimal strategies for leveraging external computational tools during reasoning. Our comprehensive experiments on AIME2024 and AIME2025 demonstrate that ReTool not only achieves superior accuracy compared to conventional text-based RL approaches, but also converges with significantly fewer training steps.
🚀 ReTool achieves accuracy of 67.0% on AIME 2024 and 49.3% on AIME 2025 based on the Qwen2.5-32B-Instruct model, outperforming the text-based RL baseline with less than 50% training steps.
@misc{feng2025retoolreinforcementlearningstrategic,
title={ReTool: Reinforcement Learning for Strategic Tool Use in LLMs},
author={Jiazhan Feng and Shijue Huang and Xingwei Qu and Ge Zhang and Yujia Qin and Baoquan Zhong and Chengquan Jiang and Jinxin Chi and Wanjun Zhong},
year={2025},
eprint={2504.11536},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.11536},
}
