🔧 Turns out the efficient way to build tool-use training data is to write the answer first. Title: ToolGrad: Efficient tool-use dataset generation with textual "gradients" URL: Google Research's ToolGrad flips the usual order of tool-use dataset generation. Here are 3 highlights. 🔄 Answer-first, not query-first Instead of generating a user instruction first and searching depth-first for a solution, ToolGrad builds a verified API workflow first, then derives the matching user prompt, no trial-and-error exploration needed. 📝 "Gradients" made of text Instead of numeric gradients, natural-language feedback from API execution reports becomes a "textual gradient," driving four modules, Proposer, Executors, Selector, and Updater, that build the workflow step by step. 🏆 Matches top proprietary models Fine-tuned on a small dataset, ToolGrad-12B scores 83.1 on the Berkeley Function Calling Leaderboard, on par with Gemini 2.5 Pro and Claude 4.5 Opus, and ahead of GPT-5. The striking part: cheaper data generation translates directly into real, competitive agent performance. #AIAgents# #ToolUse#