VILA²

(VILA-augmented-VILA) introduces a novel approach to address the limitations of data quantity and quality in training Visual Language Models (VLMs). Instead of relying on costly human annotation or distillation from proprietary models, VILA² leverages the VLM itself to iteratively refine and augment its pretraining data, leading to significant performance improvements and achieving state-of-the-art results on the MMMU leaderboard among open-sourced models.