arXiv:2508.04086v3 Announce Type: replace 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 comp
The rapid development of large language models (LLMs) and their integration with external tools is driving a critical need for efficient, high-quality tool-use datasets to advance agentic AI capabilities.
This development proposes a more efficient and reliable method for generating tool-use datasets, directly impacting the speed and quality of AI agent development, and potentially accelerating the deployment of autonomous systems.
The paradigm for creating tool-use LLM datasets shifts from 'query-first' to 'answer-first' with 'textual gradients,' reducing annotation failures and improving data generation efficiency, which directly improves AI agent learning.
- · AI Agent Developers
- · Companies building autonomous systems
- · Researchers in LLM tool-use
- · Data generation platforms
- · Traditional manual dataset annotation services
- · Inefficient tool-use data generation methods
Improved efficiency and quality of AI agent training data lead to more sophisticated and reliable AI agents.
Accelerated development and broader adoption of AI agents across various industries, collapsing white-collar workflows and SaaS layers.
The proliferation of highly capable AI agents could redefine the competitive landscape for businesses heavily reliant on complex, multi-step digital processes.
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