ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning

• URL: http://arxiv.org/abs/2504.01400v2
• Date: Thu, 14 Aug 2025 03:37:54 GMT
• Title: ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
• Authors: Xingshan Zeng, Weiwen Liu, Xu Huang, Zezhong Wang, Lingzhi Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruiming Tang, Qun Liu,
• Abstract summary: Tool learning allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks.<n>We propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning.<n>We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models.
• Score: 84.69651852838794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, existing approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning. ToolACE-R features a model-aware iterative training procedure that progressively adjust training samples based on the model's evolving capabilities to maximize its potential. Additionally, it incorporates self-refinement training corpus which emphasizes LLM's ability to iteratively refine their tool calls, optimizing performance without requiring external feedback. Furthermore, we introduce adaptive self-refinement mechanism for efficient test-time scaling, where the trained model can autonomously determine when to stop the process based on iterative self-refinement. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models. The performance of tool invocation can be further improved efficiently through adaptive self-refinement. These results highlight the effectiveness and generalizability of ToolACE-R, offering a promising direction for more efficient and scalable tool learning.

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