# ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models

arXiv: https://arxiv.org/abs/2310.02998 · HTML: https://mlsanity.com/paper/2310.02998/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Visual token pruning and efficiency, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 67 of its 91 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Visual token pruning and efficiency](https://mlsanity.com/mllm/visual-token-pruning-and-efficiency/)

## Builds on (most cited first, 30 of 67)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)

## Built on by (most cited first, 19 of 19)

- [Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models](https://mlsanity.com/paper/2402.12048/) (2024)
- [SparseLLM: Towards Global Pruning for Pre-trained Language Models](https://mlsanity.com/paper/2402.17946/) (2024)
- [A Survey on Efficient Vision-Language Models](https://mlsanity.com/paper/2504.09724/) (2025)
- [Efficient Model Editing with Task-Localized Sparse Fine-tuning](https://mlsanity.com/paper/2504.02620/) (2025)
- [Beware of Calibration Data for Pruning Large Language Models](https://mlsanity.com/paper/2410.17711/) (2024)
- [LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models](https://mlsanity.com/paper/2503.16843/) (2025)
- [Adapt-∞: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection](https://mlsanity.com/paper/2410.10636/) (2024)
- [Mutual Information Preserving Neural Network Pruning](https://mlsanity.com/paper/2411.00147/) (2024)
- [Understanding and Harnessing Sparsity in Unified Multimodal Models](https://mlsanity.com/paper/2512.02351/) (2025)
- [RSQ: Learning from Important Tokens Leads to Better Quantized LLMs](https://mlsanity.com/paper/2503.01820/) (2025)
- [The Few Govern the Many:Unveiling Few-Layer Dominance for Time Series Models](https://mlsanity.com/paper/2511.07237/) (2025)
- [You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models](https://mlsanity.com/paper/2410.20265/) (2024)
- [Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models](https://mlsanity.com/paper/2608.06901/) (2026)
- [Optimizing Vision-Language Consistency via Cross-Layer Regional Attention Alignment](https://mlsanity.com/paper/2508.00945/) (2025)
- [INTERLACE: Interleaved Layer Pruning and Efficient Adaptation in Large Vision-Language Models](https://mlsanity.com/paper/2511.19676/) (2025)
- [LinMU: Multimodal Understanding Made Linear](https://mlsanity.com/paper/2601.01322/) (2026)
- [Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models](https://mlsanity.com/paper/2603.16001/) (2026)
- [LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models](https://mlsanity.com/paper/2605.10641/) (2026)
- [Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding](https://mlsanity.com/paper/2606.25160/) (2026)

## Abstract (arXiv)

Large Vision-Language Models (LVLMs) can understand the world comprehensively by integrating rich information from different modalities, achieving remarkable advancements on various multimodal downstream tasks. However, deploying LVLMs is often problematic due to their massive computational/energy costs and carbon consumption. Such issues make it infeasible to adopt conventional iterative global pruning, which is costly due to computing the Hessian matrix of the entire large model for sparsification. Alternatively, several studies have recently proposed layer-wise pruning approaches to avoid the expensive computation of global pruning and efficiently compress model weights according to their importance within a layer. However, they often suffer from suboptimal model compression due to their lack of a global perspective. To address this limitation in recent efficient pruning methods for large models, we propose Efficient Coarse-to-Fine LayerWise Pruning (ECoFLaP), a two-stage coarse-to-fine weight pruning approach for LVLMs. We first determine the sparsity ratios of different layers or blocks by leveraging the global importance score, which is efficiently computed based on the zeroth-order approximation of the global model gradients. Then, the model performs local layer-wise unstructured weight pruning based on globally-informed sparsity ratios. We validate our proposed method across various multimodal and unimodal models and datasets, demonstrating significant performance improvements over prevalent pruning techniques in the high-sparsity regime.