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In the arXiv preprint (2606.25996), Meta's team proposes the Autodata method, where an AI agent acts as a data scientist to iteratively generate high-quality synthetic training and evaluation data. The core mechanism, Agentic Self-Instruct, forms a closed loop using an orchestrator, challenger, weak/strong solver, and judge, and introduces meta-optimization to evolve prompts, significantly improving data quality.