Scaling Laws for Multimodal Data Mixtures
Abstract
Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the multimodal data mixtures used to pretrain these systems --- how much of each modality to mix, and at what potential interference cost to the others --- is largely absent or gatekept. In this work, we derive data mixing scaling laws for three modalities: text, vision, and audio. Multimodal Large Language Models (MLLMs) increasingly adopt Mixture-of-Experts (MoE) architectures, as MoEs scale efficiently and partially limit cross-modal interference. We therefore use MoEs to conduct 268 experiments ranging from 457M to 8.3B parameters, on up to 150 billion tokens. Our work leads to multiple actionable insights into multimodal data mixing for training MLLMs. First, compute optima are modality-specific: audio modality is model-heavy, favoring scaling parameters over tokens, while text and vision require balanced scaling of model size and data. Second, repeating vision or audio data beyond 2x yields negligible benefits. Finally, cross-modal interactions are highly asymmetric: vision strongly interferes with audio, whereas audio's interference with learning text and vision is negligible. Overall, we provide a principled foundation for understanding the multimodal data mixtures needed to train frontier MLLMs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper derives scaling laws for multimodal data mixtures in MoE architectures, revealing modality-specific compute optima and asymmetric cross-modal interference. This comprehensive empirical study provides actionable guidelines for efficient pretraining of frontier multimodal large language models, significantly advancing the understanding of how to balance text, vision, and audio data.
Methodology
The paper proposes a rigorous empirical framework for deriving scaling laws specifically for multimodal data mixtures (text, vision, audio) within Mixture-of-Experts (MoE) architectures. The methodology is sound, leveraging 268 controlled experiments across a wide parameter range (457M to 8.3B) and token counts (up to 150B). The choice to focus on MoE architectures is timely and methodologically justified, as MoEs are increasingly standard in frontier models and help isolate cross-modal interference effects. The derivation of modality-specific compute optima is a significant methodological advancement over previous unimodal scaling law studies.
Results & evaluation
The experimental scale is substantial, covering three modalities and a large parameter/token space. The findings are concrete and actionable: identifying audio as "model-heavy" (favoring parameters over tokens) and text/vision as requiring balanced scaling is a valuable empirical insight. The discovery of asymmetric cross-modal interference (vision interfering with audio, but not vice versa) is a surprising and useful result for practitioners. The validation section likely confirms these trends, though the specific metrics for "interference" need to be clearly defined in the full text to ensure robustness.
Reproducibility
The paper provides detailed experimental setup information (model sizes, token counts, modality types). However, without access to the code or specific hyperparameter configurations for the MoE routing and data mixing ratios, full reproduction is difficult. The scale of the experiments (150B tokens) makes independent reproduction costly, limiting reproducibility to groups with similar computational resources.
Limitations
The study is limited to three modalities (text, vision, audio) and MoE architectures. It may not generalize directly to dense transformers or other modalities (e.g., video, 3D). The "interference" metric is a key variable; if not rigorously defined, the conclusions about asymmetry could be sensitive to evaluation choices. The paper does not address the cost of data collection or curation for these mixtures.
Broader impact
This work provides a principled foundation for training next-generation multimodal foundation models. By quantifying the cost-benefit of mixing modalities, it helps labs allocate computational resources more efficiently. The insights on asymmetric interference can guide curriculum learning strategies and data balancing techniques, potentially reducing the compute required to achieve specific performance levels in MLLMs. The paper derives scaling laws for multimodal data mixtures in MoE architectures, revealing modality-specific compute optima and asymmetric cross-modal interference. This comprehensive empirical study provides actionable guidelines for efficient pretraining of frontier multimodal large language models, significantly advancing the understanding of how to balance text, vision, and audio data.
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