| 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 |
| date | 2026-06-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.25996 |
| affiliation | AI at Meta |
| upvotes | 21 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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. |
cite
@misc{kulikov2026autodata,
title={Autodata: An agentic data scientist to create high quality synthetic data},
author={Ilia Kulikov and Chenxi Whitehouse and Tianhao Wu and Yixin Nie and Swarnadeep Saha and Eryk Helenowski and Weizhe Yuan and Olga Golovneva and Jack Lanchantin and Yoram Bachrach and Jakob Foerster and Xian Li and Han Fang and Sainbayar Sukhbaatar and Jason Weston},
year={2026},
eprint={2606.25996},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.25996}
}