We compare leading open SFT datasets, add quality annotations using MagPie, and design curation recipes leading to a high-performing leaner SFT mixture

This paper proposes the ACT data pipeline, which reduces human annotation costs by using MLLMs as annotators and error detectors, and provides a theoretical analysis to ensure effective downstream training.