  Recent advancements in large vision-language models (LVLMs), such as GPT4-V
and LLaVA, have been substantial. LLaVA's modular architecture, in particular,
offers a blend of simplicity and efficiency. Recent works mainly focus on
introducing more pre-training and instruction tuning data to improve model's
performance. This paper delves into the often-neglected aspects of data
efficiency during pre-training and the selection process for instruction tuning
datasets. Our research indicates that merely increasing the size of
pre-training data does not guarantee improved performance and may, in fact,
lead to its degradation. Furthermore, we have established a pipeline to
pinpoint the most efficient instruction tuning (SFT) dataset, implying that not
all SFT data utilized in existing studies are necessary. The primary objective
of this paper is not to introduce a state-of-the-art model, but rather to serve
as a roadmap for future research, aiming to optimize data usage during
pre-training and fine-tuning processes to enhance the performance of
vision-language models.
