Autodata: An agentic data scientist to create high quality synthetic data
Summary
Autodata is a method that enables AI agents to act as data scientists to create high-quality synthetic training data through meta-optimization, achieving improved performance across computer science, legal reasoning, and mathematical tasks.
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Paper page - Autodata: An agentic data scientist to create high quality synthetic data
Source: https://huggingface.co/papers/2606.25996 Authors:
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Abstract
Autodata enables AI agents to function as data scientists who create high-quality training data through meta-optimization, demonstrating improved performance across multiple task domains.
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such adata scientist agent, so that it learns to create even stronger data. We describe the overall formulation, and a specific practical implementation,Agentic Self-Instruct. We conduct experiments on computer science research tasks, legal reasoning tasks and reasoning with mathematical objects, where we obtain improved results compared to classicalsynthetic dataset creationmethods. Further, meta-optimizing thedata scientist agentitself delivers an even larger performance uplift. Agentic data creation provides a way to convert increasedinference computeinto higher qualitymodel training. Overall, we believe this direction has the potential to change the way we build AI data.
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