Agentic Self-Instruct

Autodata: Meta-optimized agentic data scientist for high-quality synthetic data generation

Researchers introduce Autodata, a framework that trains AI agents to act as data scientists capable of generating high-quality synthetic training and evaluation data. The method includes a meta-optimization loop (Agentic Self-Instruct) that improves the data scientist agent itself, yielding further performance gains. Experiments on CS research, legal reasoning, and mathematical reasoning tasks show improvements over classical synthetic data methods. The authors frame this as a path to converting inference compute into higher-quality training data.