This paper introduces Data Intrinsic Consistency (DIC), a self-scoring metric measuring sample-level inter-component consistency through Visual Information Consistency and Response Information Consistency modules, and DICS, an adaptive data-selection method balancing high intra-sample consistency with distributional diversity to optimize visual instruction-tuning datasets. The method reportedly matches full-dataset training performance using only 25% of the training data across multiple model architectures.
