ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
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arXiv
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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909986133639168 |
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| author | Zeng, Xingshan Liu, Weiwen Huang, Xu Wang, Zezhong Wang, Lingzhi Li, Liangyou Wang, Yasheng Shang, Lifeng Jiang, Xin Tang, Ruiming Liu, Qun |
| author_facet | Zeng, Xingshan Liu, Weiwen Huang, Xu Wang, Zezhong Wang, Lingzhi Li, Liangyou Wang, Yasheng Shang, Lifeng Jiang, Xin Tang, Ruiming Liu, Qun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning Zeng, Xingshan Liu, Weiwen Huang, Xu Wang, Zezhong Wang, Lingzhi Li, Liangyou Wang, Yasheng Shang, Lifeng Jiang, Xin Tang, Ruiming Liu, Qun Computation and Language Artificial Intelligence Machine Learning 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. |
| title | ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.01400 |