arXiv:2606.25996v1 Announce Type: cross 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 datase
The proliferation of AI models demands high-quality training and evaluation data, which is a significant bottleneck; agentic approaches to data generation are a natural next step in AI development.
This development addresses a critical bottleneck in AI development, potentially accelerating model improvement and enabling more sophisticated applications across various domains.
AI agents can now autonomously generate and meta-optimize synthetic data, reducing human effort and improving data quality for complex tasks.
- · AI developers
- · Data scientists
- · Companies with proprietary data
- · AI ethics and safety researchers
- · Manual data labeling services
- · Generative AI models producing low-quality synthetic data
Increased efficiency and quality in AI model training and evaluation due to autonomous data generation.
Faster development and deployment of advanced AI agents capable of complex reasoning and task execution.
Potential for new forms of intellectual property disputes over 'agent-generated' data and models, and an acceleration of a new digital economy built on agentic systems.
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Read at arXiv cs.LG