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October 15, 2025Open Access

LOP: Learning Optimal Pruning for Efficient On-Demand MLLMs Scaling

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This framework learns pruning strategies, improving efficiency in multimodal large language models, suggesting faster adaptation.

LOP achieves significant speedup while outpacing traditional pruning methods, dramatically increasing deployment efficiency.
Experimental results demonstrate LOP surpasses state-of-the-art pruning techniques across multiple tasks and metrics.
The LOP framework eliminates the need for expensive search-based methods, streamlining pruning strategy determination.
Optimal pruning strategies are learned adaptively, showcasing a novel approach to enhancing multimodal large language models.

Zhang et al. (2025) studied this question.

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