Researchers Slash Vision-Language Model Costs by 86% While Preserving Accuracy

Researchers introduced RCP, a pruning framework that removes up to 89% of visual tokens from large vision-language models while maintaining performance. The method uses a delayed repair mechanism to counteract information loss, reducing computational costs significantly—critical for making these AI systems more efficient and accessible.