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

Source

Source name: arXiv
Source kind: paper
Authors / organization: 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
Venue: arXiv: cs.AI
Published: 2026-06-24
Collected: 2026-06-25
URL: http://arxiv.org/abs/2606.25996v1
PDF: https://arxiv.org/pdf/2606.25996v1
DOI: UNKNOWN

Plain-Language Brief

The authors introduce Autodata, an AI agent that acts as a data scientist to create high-quality synthetic training and evaluation data. They discuss how to train this data scientist agent to improve its data creation abilities and provide a specific implementation called Agentic Self-Instruct. The experiments conducted on various tasks show improved results compared to traditional synthetic dataset creation methods, with further improvements when meta-optimizing the data scientist agent.

What the Document Says

Introduction of Autodata: AI agent for creating high quality training and evaluation data
Description of training (meta-optimization) for such a data scientist agent
Presentation of Agentic Self-Instruct as a specific practical implementation
Experiments conducted on computer science research tasks, legal reasoning tasks, and reasoning with mathematical objects
Improved results obtained compared to classical synthetic dataset creation methods
Meta-optimization of the data scientist agent delivers additional performance uplift

Key Concepts Mentioned

Autodata
AI agent
Data scientist
Synthetic data
Training
Meta-optimization
Agentic Self-Instruct
Computer science research tasks
Legal reasoning tasks
Mathematical objects

Claims Made by the Document

Autodata can create high quality training and evaluation data
Meta-optimizing the data scientist agent improves performance

Evidence Used

Experiments conducted on various tasks show improved results compared to traditional methods

Methods / Approach

Training and meta-optimization of AI agents for data creation

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.