MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and
  Training Recipe
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.
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Tianyu Yu
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Zefan Wang
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Zefan Wang
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Chongyi Wang
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Fuwei Huang
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Zhihui He
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Tianchi Cai
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Weize Chen
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Liqing Ruan
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Luoyuan Zhang
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Hanyu Liu
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Qining Guo
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Wenhao Hu
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Bingxiang He
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Jie Zhou
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Zonghao Guo
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Chi Chen
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Guoyang Zeng
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Guoyang Zeng
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Yuxuan Li
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Ganqu Cui
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Ganqu Cui
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Ning Ding
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Xu Han
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Yuan Yao
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Zhiyuan Liu
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Maosong Sun
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and
  Training Recipe
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.
MiniCPM-V 4.5, a 8B parameter multimodal large language model, achieves high performance and efficiency through a unified 3D-Resampler architecture, a unified learning paradigm, and a hybrid reinforcement learning strategy.
3D-Resampler unified learning paradigm hybrid reinforcement learning strategy multimodal large language models OpenCompass evaluation VideoMME benchmark