Autodata: An agentic data scientist to create high quality synthetic data
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Computer Science > Artificial Intelligence
Title:Autodata: An agentic data scientist to create high quality synthetic data
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: | arXiv:2606.25996 [cs.AI] |
| (or arXiv:2606.25996v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25996
arXiv-issued DOI via DataCite (pending registration)
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