MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and
  Training Recipe
Authors: name: Tianyu Yu, name: Zefan Wang, name: Chongyi Wang, name: Fuwei Huang, name: Wenshuo Ma, name: Zhihui He, name: Tianchi Cai, name: Weize Chen, name: Yuxiang Huang, name: Yuanqian Zhao, name: Bokai Xu, name: Junbo Cui, name: Yingjing Xu, name: Liqing Ruan, name: Luoyuan Zhang, name: Hanyu Liu, name: Jingkun Tang, name: Hongyuan Liu, name: Qining Guo, name: Wenhao Hu, name: Bingxiang He, name: Jie Zhou, name: Jie Cai, name: Ji Qi, name: Zonghao Guo, name: Chi Chen, name: Guoyang Zeng, name: Yuxuan Li, name: Ganqu Cui, name: Ning Ding, name: Xu Han, name: Yuan Yao, name: Zhiyuan Liu, name: Maosong Sun

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and
represent the frontier of AI development. However, their training and inference
efficiency have emerged as a core bottleneck in making MLLMs more accessible
and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B
parameter model designed for high efficiency and strong performance. We
introduce three core improvements in model architecture, data strategy and
training method: a unified 3D-Resampler model architecture for highly compact
encoding over images and videos, a unified learning paradigm for document
knowledge and text recognition without heavy data engineering, and a hybrid
reinforcement learning strategy for proficiency in both short and long
reasoning modes. Comprehensive experimental results in OpenCompass evaluation
show that MiniCPM-V 4.5 surpasses widely used proprietary models such as
GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL
72B. Notably, the strong performance is achieved with remarkable efficiency.
For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves
state-of-the-art performance among models under 30B size, using just 46.7\% GPU
memory cost and 8.7\% inference time of Qwen2.5-VL 7B.