ToRL: Scaling Tool-Integrated RL

Xuefeng Li; Haoyang Zou; Pengfei Liu

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arXiv · 2503.23383

ToRL: Scaling Tool-Integrated RL

Xuefeng Li·Haoyang Zou·Pengfei Liu

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Abstract

We introduce ToRL (Tool-Integrated Reinforcement Learning), a framework for training large language models (LLMs) to autonomously use computational tools via reinforcement learning. Unlike supervised fine-tuning, ToRL allows models to explore and discover optimal strategies for tool use. Experiments with Qwen2.5-Math models show significant improvements: ToRL-7B reaches 43.3\% accuracy on AIME~24, surpassing reinforcement learning without tool integration by 14\% and the best existing Tool-Integrated Reasoning (TIR) model by 17\%. Further analysis reveals emergent behaviors such as strategic tool invocation, self-regulation of ineffective code, and dynamic adaptation between computational and analytical reasoning, all arising purely through reward-driven learning.

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BibTeX RIS

Xuefeng Li, Haoyang Zou, Pengfei Liu. 2025-03-30. ToRL: Scaling Tool-Integrated RL. https://arxiv.org/abs/2503.23383

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