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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04086 |
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| _version_ | 1866913078540500992 |
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| author | Zhou, Zhongyi Uehara, Kohei Zhang, Haoyu Zhou, Jingtao Gu, Lin Du, Ruofei Xu, Zheng Harada, Tatsuya |
| author_facet | Zhou, Zhongyi Uehara, Kohei Zhang, Haoyu Zhou, Jingtao Gu, Lin Du, Ruofei Xu, Zheng Harada, Tatsuya |
| contents | Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS). This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-500, a dataset generated with more complex tool use, lower cost, and almost 100% pass rate. Experiments show that ToolGrad models outperform those trained on expensive baseline datasets and proprietary LLMs. The ToolGrad source code, dataset, and models are available at https://github.com/zhongyi-zhou/toolgrad. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients" Zhou, Zhongyi Uehara, Kohei Zhang, Haoyu Zhou, Jingtao Gu, Lin Du, Ruofei Xu, Zheng Harada, Tatsuya Computation and Language Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS). This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-500, a dataset generated with more complex tool use, lower cost, and almost 100% pass rate. Experiments show that ToolGrad models outperform those trained on expensive baseline datasets and proprietary LLMs. The ToolGrad source code, dataset, and models are available at https://github.com/zhongyi-zhou/toolgrad. |
| title | ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients" |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.04086 |