Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation
Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.
Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.
- ToolGrad builds verified tool chains first, then generates matching queries, eliminating failed DFS exploration.
- Pass rate on ToolBench data generation rose from 63.8% (query-first) to 99.8%.
- ToolGrad-12B scored 83.1 on BFCL, outperforming its Gemini 2.5 Flash-Lite teacher.
- Only 500 training samples produced frontier-competitive tool-use performance in open 12B models.
- Apache-2.0 code, ToolGrad-500 dataset, and 1B/4B/12B models are publicly released.
- Vendors
- Organizations
- University of TokyoRIKEN AIPTohoku University
From the original article
3 min read at marktechpost.com
Google Research has released ToolGrad, an ACL 2026 Findings framework that inverts tool-use dataset generation: it builds a verified API chain first, then writes the matching user query. Guided by textual "gradients" from a 4-module propose-execute-select-update loop, ToolGrad reaches a 99. 8% pass rate on ToolBench versus 63.
By Michal Sutter · marktechpost.com
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