Google ToolGrad generates tool-use datasets by writing answers before queries
Google researchers present ToolGrad, a framework that reverses tool-use dataset generation by first producing ground-truth tool-use chains, then annotating matching user prompts. Presented at ACL 2026, it generates longer-horizon data at lower cost than search-based baselines like ToolBench and ToolACE, and models trained on it match proprietary LLMs on out-of-distribution, unseen-tool datasets.
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“ToolGrad first generates a ground-truth tool-use chain and then annotates its corresponding user prompt.”
“LLMs trained on our generated data also outperform those trained on baseline methods, and even match SoTA proprietary LLMs on out-of-distribution (OOD) datasets with unseen tools.”
Summary last validated Sep 11, 2026
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