  In recent years, multimodal large language models (MLLMs) have shown strong
potential in real-world applications. They are developing rapidly due to their
remarkable ability to comprehend multimodal information and their inherent
powerful cognitive and reasoning capabilities. Among MLLMs, vision language
models (VLM) stand out for their ability to understand vision information.
However, the scaling trend of VLMs under the current mainstream paradigm has
not been extensively studied. Whether we can achieve better performance by
training even larger models is still unclear. To address this issue, we
conducted experiments on the pretraining stage of MLLMs. We conduct our
experiment using different encoder sizes and large language model (LLM) sizes.
Our findings indicate that merely increasing the size of encoders does not
necessarily enhance the performance of VLMs. Moreover, we analyzed the effects
of LLM backbone parameter size and data quality on the pretraining outcomes.
Additionally, we explored the differences in scaling laws between LLMs and
VLMs.
