Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Yu, Tianyu, Wang, Zefan, Wang, Chongyi, Huang, Fuwei, Ma, Wenshuo, He, Zhihui, Cai, Tianchi, Chen, Weize, Huang, Yuxiang, Zhao, Yuanqian, Xu, Bokai, Cui, Junbo, Xu, Yingjing, Ruan, Liqing, Zhang, Luoyuan, Liu, Hanyu, Tang, Jingkun, Liu, Hongyuan, Guo, Qining, Hu, Wenhao, He, Bingxiang, Zhou, Jie, Cai, Jie, Qi, Ji, Guo, Zonghao, Chen, Chi, Zeng, Guoyang, Li, Yuxuan, Cui, Ganqu, Ding, Ning, Han, Xu, Yao, Yuan, Liu, Zhiyuan, Sun, Maosong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416257511