StepTool: A Step-grained Reinforcement Learning Framework for Tool Learning in LLMs

Yuanqing Yu, Zhefan Wang, Weizhi Ma, Zhicheng Guo, Jingtao Zhan, Shuai Wang, Chuhan Wu, Zhiqiang Guo, Min Zhang

Keywords: Tool Learning, Large Language Models, Reinforcement Learning

TL;DR: We introduce StepTool, a step-grained reinforcement learning framework that enhances tool learning in LLMs through tailored step-grained rewards and step-grained optimization, achieving superior performance in solving complex tasks.

Abstract: Despite having powerful reasoning and inference capabilities, Large Language Models (LLMs) still need external tools to acquire real-time information or domain-specific expertise to solve complex tasks, which is referred to as tool learning. Existing tool learning methods primarily rely on tuning with expert trajectories, focusing on token-sequence learning from a linguistic perspective. However, there are several challenges: 1) imitating static trajectories limits their ability to generalize to new tasks. 2) even expert trajectories can be suboptimal, and better solution paths may exist. In this work, we introduce StepTool, a novel step-grained reinforcement learning framework to improve tool learning in LLMs. It consists of two components: Step-grained Reward Shaping, which assigns rewards at each tool interaction based on tool invocation success and its contribution to the task, and Step-grained Optimization, which uses policy gradient methods to optimize the model in a multi-step manner. Experimental results demonstrate that StepTool significantly outperforms existing methods in multi-step, tool-based tasks, providing a robust solution for complex task environments.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 10418