Google's ToolGrad generates tool-use datasets answer-first, hitting near 100% pass rate
DuRuofei · x · 2026-09-11
Google, University of Tokyo, and RIKEN AIP researchers introduced ToolGrad, an efficient framework for generating LLM tool-use datasets, released at ACL 2026 Findings with open data and code.
- Key idea: inverts the usual pipeline — it first generates ground-truth tool-use chains using textual "gradients", then constructs prompts from them (answer-first instead of question-first).
- Results: near 100% pass rate for generated data, lower generation cost, and improved LLM tool-use performance after training.
- Data format: each sample includes a query, a tool-call graph, and step-by-step tool invocations, covering multi-tool scenarios like news search, company lookups, domain monitoring, and TikTok search.
Led by Zhongyi Zhou; arXiv paper, code, and dataset are all available.
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