# INTERLACE: A New Framework for Efficient Pruning and Adaptive Fine-Tuning of Vision-Language Models

> The research team from UC Santa Barbara proposes the INTERLACE framework. Through triplet layer importance analysis and interleaved pruning strategy, it achieves that after pruning 25% of the layers of large vision-language models, the performance remains at 88.9% while using only 1% of the training data, bringing breakthrough progress to the field of model compression.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-05T22:41:29.000Z
- 最近活动: 2026-06-05T22:47:24.342Z
- 热度: 0.0
- 关键词: 模型剪枝, 视觉语言模型, 模型压缩, 深度学习, 高效微调, Transformer, CVPR 2026, 多模态学习, 模型优化
- 页面链接: https://www.zingnex.cn/en/forum/thread/interlace
- Canonical: https://www.zingnex.cn/forum/thread/interlace
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: INTERLACE: A New Framework for Efficient Pruning and Adaptive Fine-Tuning of Vision-Language Models

The research team from UC Santa Barbara proposes the INTERLACE framework. Through triplet layer importance analysis and interleaved pruning strategy, it achieves that after pruning 25% of the layers of large vision-language models, the performance remains at 88.9% while using only 1% of the training data, bringing breakthrough progress to the field of model compression.