ToolSample: Dual Dynamic Sampling Methods with Curriculum Learning for RL-based Tool Learning

• URL: http://arxiv.org/abs/2509.14718v1
• Date: Thu, 18 Sep 2025 08:04:49 GMT
• Title: ToolSample: Dual Dynamic Sampling Methods with Curriculum Learning for RL-based Tool Learning
• Authors: Zihao Feng, Xiaoxue Wang, Bowen Wu, Hailong Cao, Tiejun Zhao, Qun Yu, Baoxun Wang,
• Abstract summary: This paper introduces Dynamic Sampling with Curriculum Learning (DSCL), a framework specifically designed to address this challenge.<n>DSCL features two core components: Reward-Based Dynamic Sampling, which uses multi-dimensional reward statistics (mean and variance) to prioritize valuable data, and Task-Based Dynamic Curriculum Learning, which adaptively focuses training on less-mastered sub-tasks.
• Score: 21.358546649313595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While reinforcement learning (RL) is increasingly used for LLM-based tool learning, its efficiency is often hampered by an overabundance of simple samples that provide diminishing learning value as training progresses. Existing dynamic sampling techniques are ill-suited for the multi-task structure and fine-grained reward mechanisms inherent to tool learning. This paper introduces Dynamic Sampling with Curriculum Learning (DSCL), a framework specifically designed to address this challenge by targeting the unique characteristics of tool learning: its multiple interdependent sub-tasks and multi-valued reward functions. DSCL features two core components: Reward-Based Dynamic Sampling, which uses multi-dimensional reward statistics (mean and variance) to prioritize valuable data, and Task-Based Dynamic Curriculum Learning, which adaptively focuses training on less-mastered sub-tasks. Through extensive experiments, we demonstrate that DSCL significantly improves training efficiency and model performance over strong baselines, achieving a 3.29\% improvement on the BFCLv3 benchmark. Our method provides a tailored solution that effectively leverages the complex reward signals and sub-task dynamics within tool learning to achieve superior results.

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