Compositional training has been the de-facto paradigm in existing Multimodal
Large Language Models (MLLMs), where pre-trained vision encoders are connected
with pre-trained LLMs through continuous multimodal pre-training. However, the
multimodal scaling property of this paradigm remains difficult to explore due
to the separated training. In this paper, we focus on the native training of
MLLMs in an end-to-end manner and systematically study its design space and
scaling property under a practical setting, i.e., data constraint. Through
careful study of various choices in MLLM, we obtain the optimal
meta-architecture that best balances performance and training cost. After that,
we further explore the scaling properties of the native MLLM and indicate the
positively correlated scaling relationship between visual encoders and LLMs.
Based on these findings, we propose a native MLLM called NaViL, combined with a
simple and cost-effective recipe. Experimental results on 14 multimodal
benchmarks confirm the competitive performance of NaViL against existing MLLMs.
Besides that, our findings and results provide in-depth insights for the future
study of native MLLMs.