POINTS: Improving Your Vision-language Model with Affordable Strategies
Authors: name: Yuan Liu, name: Zhongyin Zhao, name: Ziyuan Zhuang, name: Le Tian, name: Xiao Zhou, name: Jie Zhou

In recent years, vision-language models have made significant strides,
excelling in tasks like optical character recognition and geometric
problem-solving. However, several critical issues remain: 1) Proprietary models
often lack transparency about their architectures, while open-source models
need more detailed ablations of their training strategies. 2) Pre-training data
in open-source works is under-explored, with datasets added empirically, making
the process cumbersome. 3) Fine-tuning often focuses on adding datasets,
leading to diminishing returns. To address these issues, we propose the
following contributions: 1) We trained a robust baseline model using the latest
advancements in vision-language models, introducing effective improvements and
conducting comprehensive ablation and validation for each technique. 2)
Inspired by recent work on large language models, we filtered pre-training data
using perplexity, selecting the lowest perplexity data for training. This
approach allowed us to train on a curated 1M dataset, achieving competitive
performance. 3) During visual instruction tuning, we used model soup on
different datasets when adding more datasets yielded marginal improvements.
These innovations resulted in a 9B parameter model that performs competitively
with state-of-the-art models. Our strategies are efficient and lightweight,
making them easily adoptable by the community.