[2606.25996] Autodata: An agentic data scientist to create high quality synthetic data

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Computer Science > Artificial Intelligence

arXiv:2606.25996 (cs)

[Submitted on 24 Jun 2026]

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

Authors:Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu, Yixin Nie, Swarnadeep Saha, Eryk Helenowski, Weizhe Yuan, Olga Golovneva, Jack Lanchantin, Yoram Bachrach, Jakob Foerster, Xian Li, Han Fang, Sainbayar Sukhbaatar, Jason Weston<br>View a PDF of the paper titled Autodata: An agentic data scientist to create high quality synthetic data, by Ilia Kulikov and 14 other authors

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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.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.25996 [cs.AI]

(or<br>arXiv:2606.25996v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.25996

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Jason Weston [view email]<br>[v1]<br>Wed, 24 Jun 2026 16:08:31 UTC (19,889 KB)

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