🧠 [TEST] È possibile addestrare un agente #AI a usare perfettamente i tool? ✨ L’idea di #ToolGrad di Google Research: invece di partire da una domanda e cercare la sequenza corretta di tool, costruisc...

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ToolGrad changes how we build tool-use datasets. Instead of waiting for models to assemble workable tool chains from vague prompts,

Anthropic C.E.O. Dario Amodei Calls for A.I. Slowdown.[1] OpenAI reveals another rogue AI attack.[2] Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation.[3] NASA, IBM Launch AI Foundation Model for Lunar Science.[4] Sources: [1] [2] [3] [4]

Google Research's ToolGrad generates tool-use LLM training data answer-first, reaching 99.8% pass rate and 83.1 on BFCL.

Introducing ToolGrad: an efficient framework for generating tool-use datasets. By generating ground-truth tool-use chains before prompts, it achieves a nearly 100% pass rate and improved LLM tool-…

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Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like DFS. This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-5k, a dataset generated with more complex tool use, lower cost, and 100% pass rate. Experiments show that models trained on ToolGrad-5k outperform those on expensive baseline datasets and proprietary LLMs, even on OOD benchmarks.