PORTool: Tool-Use LLM Training with Rewarded Tree

• URL: http://arxiv.org/abs/2510.26020v1
• Date: Wed, 29 Oct 2025 23:28:53 GMT
• Title: PORTool: Tool-Use LLM Training with Rewarded Tree
• Authors: Feijie Wu, Weiwu Zhu, Yuxiang Zhang, Soumya Chatterjee, Jiarong Zhu, Fan Mo, Rodin Luo, Jing Gao,
• Abstract summary: We propose a reinforcement learning (RL) method that encourages a tool-use LLM to explore various trajectories yielding the correct answer.<n>A shared step across different trajectories receives the same reward, while different steps under the same fork receive different rewards.<n>Experiments utilize 17 tools to address user queries, covering both time-sensitive and time-invariant topics.
• Score: 11.154654446183455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current tool-use large language models (LLMs) are trained on static datasets, enabling them to interact with external tools and perform multi-step, tool-integrated reasoning, which produces tool-call trajectories. However, these models imitate how a query is resolved in a generic tool-call routine, thereby failing to explore possible solutions and demonstrating limited performance in an evolved, dynamic tool-call environment. In this work, we propose PORTool, a reinforcement learning (RL) method that encourages a tool-use LLM to explore various trajectories yielding the correct answer. Specifically, this method starts with generating multiple rollouts for a given query, and some of them share the first few tool-call steps, thereby forming a tree-like structure. Next, we assign rewards to each step, based on its ability to produce a correct answer and make successful tool calls. A shared step across different trajectories receives the same reward, while different steps under the same fork receive different rewards. Finally, these step-wise rewards are used to calculate fork-relative advantages, blended with trajectory-relative advantages, to train the LLM for tool use. The experiments utilize 17 tools to address user queries, covering both time-sensitive and time-invariant topics. We conduct ablation studies to systematically justify the necessity and the design robustness of step-wise rewards. Furthermore, we compare the proposed PORTool with other training approaches and demonstrate significant improvements in final accuracy and the number of tool-call steps.

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