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ToolGrad introduces an efficient method for generating tool-use datasets using textual gradients, enabling better LLM training with lower cost and improved performance on out-of-distribution tasks.
AgentGrad improves multi-agent prompt optimization by using sequential intervention to identify target agents and semantic clustering of textual gradients, achieving state-of-the-art performance and reducing optimization time by 2.5 times.
Introduces ToolGrad, an agentic framework that generates, evaluates, and refines tool-use trajectories using textual 'gradients', achieving near 100% pass rate and lower cost for dataset generation. Accepted at ACL 2026.