Year: 2026
Type: conference-paper
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.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166856381