Scaling Native Multimodal Pre-Training From Scratch
The research derives compute-optimal scaling laws for training vision-language models by analyzing how computational resources are allocated between language and multimodal objectives. It found that language learning scales predictably regardless of data composition, while multimodal learning is highly sensitive to the text-to-image ratio. This leads to a Pareto frontier guiding optimal model scaling.