Imagen de Portada

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Huang, Yiran, Thede, Lukas, Mancini, Massimiliano, Xu, Wenjia, Akata, Zeynep
Formato:	Preprint
Publicado:	2026
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study
por: Huang, Yiran, et al.
Publicado: (2025)

Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers
por: Huang, Yiran, et al.
Publicado: (2026)

WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
por: Thede, Lukas, et al.
Publicado: (2025)

Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)
por: Girrbach, Leander, et al.
Publicado: (2024)

Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
por: Liu, Yudong, et al.
Publicado: (2025)

A Systematic Study of In-the-Wild Model Merging for Large Language Models
por: Hitit, Oğuz Kağan, et al.
Publicado: (2025)

Vision-by-Language for Training-Free Compositional Image Retrieval
por: Karthik, Shyamgopal, et al.
Publicado: (2023)

PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
por: He, Bowei, et al.
Publicado: (2025)

Deterministic Differentiable Structured Pruning for Large Language Models
por: Huang, Weiyu, et al.
Publicado: (2026)

Large Language Model Pruning
por: Huang, Hanjuan, et al.
Publicado: (2024)

IG-Pruning: Input-Guided Block Pruning for Large Language Models
por: Qiao, Kangyu, et al.
Publicado: (2025)

NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models
por: Li, Shengrui, et al.
Publicado: (2024)

DLP: Dynamic Layerwise Pruning in Large Language Models
por: Chen, Yuli, et al.
Publicado: (2025)

Pruning Large Language Models with Semi-Structural Adaptive Sparse Training
por: Huang, Weiyu, et al.
Publicado: (2024)

Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
por: Wu, Guangxin, et al.
Publicado: (2026)

MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models
por: Cheng, Hongrong, et al.
Publicado: (2024)

KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Models
por: Lv, Bo, et al.
Publicado: (2024)

High-Fidelity Pruning for Large Language Models
por: Zhu, Yijun, et al.
Publicado: (2026)

DarwinLM: Evolutionary Structured Pruning of Large Language Models
por: Tang, Shengkun, et al.
Publicado: (2025)

Instruction-Following Pruning for Large Language Models
por: Hou, Bairu, et al.
Publicado: (2025)

Saliency-driven Dynamic Token Pruning for Large Language Models
por: Tao, Yao, et al.
Publicado: (2025)

Beware of Calibration Data for Pruning Large Language Models
por: Ji, Yixin, et al.
Publicado: (2024)

Exploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models
por: Siam, M. K. Khalidi, et al.
Publicado: (2026)

RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models
por: Haruta, Shuichiro, et al.
Publicado: (2025)

Olica: Efficient Structured Pruning of Large Language Models without Retraining
por: He, Jiujun, et al.
Publicado: (2025)

DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
por: Gao, Shangqian, et al.
Publicado: (2024)

Pruning Multilingual Large Language Models for Multilingual Inference
por: Kim, Hwichan, et al.
Publicado: (2024)

Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models
por: Li, Jianwei, et al.
Publicado: (2023)

Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
por: Bhuiyan, Samiul Basir, et al.
Publicado: (2025)

Adaptive Pruning for Large Language Models with Structural Importance Awareness
por: Zheng, Haotian, et al.
Publicado: (2024)

Sample-aware Adaptive Structured Pruning for Large Language Models
por: Kong, Jun, et al.
Publicado: (2025)

PIP: Perturbation-based Iterative Pruning for Large Language Models
por: Cao, Yi, et al.
Publicado: (2025)

Self-Data Distillation for Recovering Quality in Pruned Large Language Models
por: Thangarasa, Vithursan, et al.
Publicado: (2024)

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models
por: Wang, Zekun, et al.
Publicado: (2023)

Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions
por: Huang, Nannan, et al.
Publicado: (2025)

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning
por: Gao, Shangqian, et al.
Publicado: (2025)

Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
por: Wang, Ziyan, et al.
Publicado: (2025)

Entropy-Based Block Pruning for Efficient Large Language Models
por: Yang, Liangwei, et al.
Publicado: (2025)

Relative Kinetic Utility for Reasoning-Aware Structural Pruning in Large Language Models
por: Qian, Tianhao
Publicado: (2026)

Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models
por: Thede, Lukas, et al.
Publicado: (2024)