The field of multimodal large language models is rapidly evolving, with a focus on improving generalization and reasoning capabilities. Recent developments have centered around novel data mixing strategies, such as online data mixing and multi-domain data mixtures, which have shown significant promise in enhancing model performance. Additionally, researchers have proposed innovative architectures, including mixture-of-experts and task-aware mixture-of-experts, to mitigate task objective conflicts and improve overall coordination. Noteworthy papers in this area include Mixed-R1, which presents a unified reward perspective for reasoning capability in multimodal large language models, and Mixpert, which introduces an efficient mixture-of-vision-experts architecture for task-specific fine-tuning. Furthermore, the development of benchmarks like VS-Bench has enabled more comprehensive evaluations of vision language models in strategic reasoning and decision-making. Overall, these advancements are driving the field towards more robust and versatile multimodal models.