The recent developments in the field of vision-language models (VLMs) and multimodal large language models (MLLMs) have shown a significant shift towards enhancing both visual and linguistic capabilities through innovative training strategies and model architectures. There is a notable trend towards the integration of multiple modalities without the need for extensive retraining, which reduces computational costs and deployment overhead. This is achieved through novel frameworks like VisionFuse, which leverages existing models to enhance visual perception without additional training. Additionally, the field is witnessing advancements in implicit model fusion techniques, such as Weighted-Reward Preference Optimization (WRPO), which effectively transfers capabilities between models without the complexities of traditional fusion methods. Another area of progress is the mitigation of language reasoning degradation in multimodal models, where training-free techniques are being developed to preserve and even enhance language reasoning abilities. Furthermore, the introduction of perception tokens in multimodal language models is expanding the scope of visual reasoning tasks, allowing models to generate intermediate representations that assist in complex reasoning processes. Lastly, the development of scalable multi-modal generators, such as Liquid, demonstrates that large language models can be effectively adapted to handle both visual and linguistic tasks, paving the way for more integrated and efficient multimodal systems.

Noteworthy papers include VARCO-VISION for its innovative bilingual vision-language model and the release of Korean evaluation datasets, and Liquid for its scalable multi-modal generation paradigm that integrates visual and linguistic tasks seamlessly.