Apple researchers developed MixAtlas to optimize data mixtures for multimodal LLM midtraining. The framework uses systematic domain decomposition and small proxy models to improve sample efficiency. It replaces the common practice of tuning mixtures based on single perspectives like task type. This approach allows developers to refine training sets with less compute and better generalization.