Poster

MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Tianyu Yu ⋅ Zefan Wang ⋅ Chongyi Wang ⋅ Fuwei Huang ⋅ Wenshuo Ma ⋅ Zhihui He ⋅ Tianchi Cai ⋅ Weize Chen ⋅ Yuxiang Huang ⋅ Ranchi Zhao ⋅ Bokai Xu ⋅ Junbo Cui ⋅ Yingjing Xu ⋅ Liqing Ruan ⋅ Luoyuan Zhang ⋅ Hanyu Liu ⋅ Jingkun Tang ⋅ Hongyuan Liu ⋅ Qining Guo ⋅ Wenhao Hu ⋅ Bingxiang He ⋅ Jie Zhou ⋅ Jie Cai ⋅ Ji Qi ⋅ Zonghao Guo ⋅ Chi Chen ⋅ Guoyang Zeng ⋅ Yuxuan Li ⋅ Ganqu Cui ⋅ Ning Ding ⋅ Xu Han ⋅ Yuan Yao ⋅ Zhiyuan Liu ⋅ Maosong Sun

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.