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ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"

arXiv cs.CL / 5/4/2026

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Key Points

  • The paper introduces ToolGrad, an agentic framework for generating tool-use datasets that avoids failure-prone, annotation-heavy pipelines used in prior “query-first” approaches.
  • Instead of creating a user query and then adding complex tool-use annotations, ToolGrad uses iterative steps guided by textual “gradients” to build valid tool-use chains first (“answer-first”), and then synthesizes the matching user queries.
  • ToolGrad produced ToolGrad-500, showing more complex tool usage, lower generation cost, and nearly a 100% pass rate for generated samples.
  • Experiments indicate models trained on ToolGrad’s datasets outperform models trained on costly baseline datasets and even some proprietary LLM-based datasets.
  • The authors provide the source code, dataset, and models publicly via GitHub for replication and further research.

Abstract

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.

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