MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining
Apple Machine Learning Journal / 4/16/2026
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Key Points
- MixAtlas proposes an uncertainty-aware data mixture optimization method designed to improve how multimodal LLMs learn during midtraining.
- The approach uses model uncertainty signals to adjust data mixture weights, aiming to focus training on more informative or suitable data sources as learning progresses.
- The work is framed around multimodal settings, indicating its applicability to training regimes that combine different input modalities (e.g., vision and language).
- MixAtlas is presented as a research paper published in April 2026 and made available via OpenReview, with authors spanning multiple institutions and collaborators.
- The contribution targets training dynamics rather than just architecture or inference-time changes, suggesting potential downstream benefits for systems that require stronger multimodal representation learning.
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026.
Principled domain reweighting can substantially improve sample efficiency and downstream generalization; however, data-mixture optimization for multimodal pretraining remains underexplored. Current multimodal training recipes tune mixtures from only a single perspective such as data format or task type. We introduce MixAtlas, a principled framework for compute-efficient multimodal mixture optimization via systematic domain decomposition and smaller proxy models…
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