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Machine Learning

Scaling Native Multimodal Pre-Training From Scratch

Haoyuan Wu, Aoqi Wu, Hai Wang, Jiajia Wu, Jinxiang Ou, Bei Yu

Featured July 30, 2026

Original Paper

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Simply

Training models from scratch on mixed images and text helps them learn better, and this study shows how to pick the perfect model size and data amount to get the best results for a given computer budget.

In depth
The paper systematically characterizes the scaling properties of native multimodal pre-training, demonstrating that optimal model size and token count adhere to power laws under a fixed computational budget. It reveals distinct scaling behaviors for language and multimodal objectives, with the latter being highly sensitive to data composition, leading to a derived efficiency frontier for optimal resource allocation.

Key Takeaways

  • 1
    The study establishes compute-optimal scaling laws for native multimodal pre-training, guiding resource allocation between model size and data.
  • 2
    It identifies distinct scaling behaviors for language (composition-invariant) and multimodal (composition-variant) objectives, necessitating a joint optimization strategy.
  • 3
    The research empirically demonstrates that native multimodal pre-training preserves core language capabilities, enhances pure-text spatial reasoning, and enables robust multimodal in-context learning.

Conceptual Flow

HIGH LEVEL
1
Methodology: Finding the Best Recipe

The authors tested many different model sizes and data amounts to find the perfect mix that gives the best learning for a set amount of computer power.

Computer Power
Model Size
Training Data
Test Many Combinations
Best Model Size
Best Training Data
2
Results: Different Learning Styles

They found that models learn text and images differently; text learning is steady, but image learning needs more data as the image-to-text mix changes.

Text Learning
Image Learning
Data Mix
Compare Learning Patterns
Text Learning: Steady
Image Learning: Changes with Mix