The recent advancements in the field of vision-language models (VLMs) have shown a significant shift towards enhancing multimodal understanding through innovative architectural designs and novel training strategies. A common theme across the latest research is the integration of hierarchical and dynamic processing mechanisms to better capture the diverse granularity of visual and linguistic data. This includes the use of mixture-of-experts models, hierarchical window transformers, and feature pyramid tokenization, which aim to improve the model's ability to handle high-resolution images, complex compositional generalizations, and open vocabulary semantic segmentation. These approaches not only enhance the model's performance across various tasks but also demonstrate a more efficient use of parameters, making them scalable and practical for real-world applications. Notably, the emphasis on consistency in compositional generalization across multiple levels and the incorporation of high-resolution feature pyramids are particularly noteworthy for advancing the state-of-the-art in multimodal learning. The public availability of code and pre-trained models further facilitates the adoption and exploration of these cutting-edge techniques by the research community.