Scaling Laws for Multimodal Data Mixtures
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…
This research investigates scaling laws for multimodal data mixtures in training advanced AI systems, focusing on optimal data composition and model architecture trade-offs for text, vision, and audio modalities.
What happened
Researchers conducted 268 experiments with MoE models ranging from 457M to 8.3B parameters, analyzing how different data mixtures affect multimodal large language model (MLLM) training. They tested up to 150 billion tokens across text, vision, and audio modalities.
Why it matters
The findings provide practical guidelines for optimizing multimodal AI training, helping developers balance computational resources and data composition to improve model performance while managing interference between modalities.
- Prioritize parameter scaling for audio modalities
- Limit vision/audio data repetition beyond 2x
- Account for asymmetric interference in multimodal design
- Use MoE architectures to manage cross-modal conflicts
The bigger picture
The study enables more efficient training strategies for multimodal systems by quantifying optimal data composition and highlighting architecture trade-offs, potentially reducing computational costs while maintaining performance.
This work establishes foundational principles for optimizing multimodal data mixtures in large AI systems, offering actionable insights for balancing computational resources and data composition in MLLM training.
Technical details
- Mixture-of-Experts (MoE) architectures
- Scaling laws for data mixture optimization
- Cross-modal interference analysis
- Parameter-to-token scaling trade-offs
FAQ
What are the main findings about data repetition?
Repeating vision or audio data beyond 2x yields negligible performance improvements.
How do modalities interfere with each other?
Vision significantly disrupts audio training, while audio has minimal impact on text/vision learning.
What architecture was used for experiments?
Mixture-of-Experts (MoE) architectures were employed to enable efficient scaling and manage cross-modal interference.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.CL (NLP) on Oct 14, 2026.
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