Scaling Native Multimodal Pre-Training From Scratch
Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world. Native multimodal pre-training avoids this limitation by training models from scratch on multimodal inputs, thereby achieving deep cross-modal integration and mitigating optimization asymmetries inherent to traditional late-fusion architectures. Despite these advantages, the scaling properties of this paradigm remain incompletely characterized. To address this gap, we investigate the optimal model size and token count for training a Transformer-based vision-language model under a fixed computational budget. Our...
| Authors | Haoyuan Wu, Aoqi Wu, Hai Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 July 2026 |
| Last updated at source | 8 October 2026 |
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