AI RESEARCH
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
arXiv CS.CL
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ArXi:2604.24380v1 Announce Type: new While Large Vision Language Models (LVLMs) nstrate impressive capabilities, their substantial computational and memory requirements pose deployment challenges on resource-constrained edge devices. Current parameter reduction techniques primarily involve