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

Researchers introduce Autodata, an AI agent that can act as a data scientist to create high-quality synthetic data for various tasks like computer science research, legal reasoning, and mathematical object reasoning. The agent is trained using a method called Agentic Self-Instruct, and the results show improved performance compared to traditional synthetic dataset creation methods.

What the Document Says

Introduced: Autodata
Autodata enables AI agents to act as data scientists
Training method: meta-optimization
Learns to create stronger data
Specific implementation: Agentic Self-Instruct
Experiments conducted on computer science tasks, legal reasoning tasks, and reasoning with mathematical objects
Improved results compared to classical synthetic dataset creation methods

Key Concepts Mentioned

Autodata
AI agent
data scientist
meta-optimization
Agentic Self-Instruct
synthetic data
computer science tasks
legal reasoning tasks
mathematical objects

Claims Made by the Document

Autodata can act as a data scientist
Improved results with Autodata compared to classical synthetic dataset creation methods

Evidence Used

Training an AI agent to create high-quality training and evaluation data
Experiments showing improved results

Methods / Approach

meta-optimization
Agentic Self-Instruct

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.