Autodata: An agentic data scientist to create high quality synthetic data

• URL: http://arxiv.org/abs/2606.25996v2
• Date: Thu, 25 Jun 2026 13:26:50 GMT
• Title: Autodata: An agentic data scientist to create high quality synthetic data
• Abstract summary: We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data.<n>We conduct experiments on computer science research tasks, legal reasoning tasks and reasoning with mathematical objects.<n>We believe this direction has the potential to change the way we build AI data.
• Score: 73.52340524237377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a data 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 classical synthetic dataset creation methods. Further, meta-optimizing the data scientist agent itself delivers an even larger performance uplift. Agentic data creation provides a way to convert increased inference compute into higher quality model training. Overall, we believe this direction has the potential to change the way we build AI data.

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