Saved in:
| Main Authors: | Yi, Kai, Richtárik, Peter |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18980 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
by: Ilin, Ivan, et al.
Published: (2025)
by: Ilin, Ivan, et al.
Published: (2025)
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
by: Yi, Kai, et al.
Published: (2024)
by: Yi, Kai, et al.
Published: (2024)
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
by: Kang, Yuhan, et al.
Published: (2025)
by: Kang, Yuhan, et al.
Published: (2025)
Exploring Federated Pruning for Large Language Models
by: Guo, Pengxin, et al.
Published: (2025)
by: Guo, Pengxin, et al.
Published: (2025)
Large Language Model Pruning
by: Huang, Hanjuan, et al.
Published: (2024)
by: Huang, Hanjuan, et al.
Published: (2024)
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
by: Shrestha, Safal, et al.
Published: (2026)
by: Shrestha, Safal, et al.
Published: (2026)
Wanda++: Pruning Large Language Models via Regional Gradients
by: Yang, Yifan, et al.
Published: (2025)
by: Yang, Yifan, et al.
Published: (2025)
The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
by: Lu, Yao, et al.
Published: (2025)
by: Lu, Yao, et al.
Published: (2025)
LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
by: Mozaffari, Mohammad, et al.
Published: (2026)
by: Mozaffari, Mohammad, et al.
Published: (2026)
GPrune-LLM: Generalization-Aware Structured Pruning for Large Language Models
by: Liu, Xiaoyun, et al.
Published: (2026)
by: Liu, Xiaoyun, et al.
Published: (2026)
A Novel Unified Parametric Assumption for Nonconvex Optimization
by: Riabinin, Artem, et al.
Published: (2025)
by: Riabinin, Artem, et al.
Published: (2025)
Beware of Calibration Data for Pruning Large Language Models
by: Ji, Yixin, et al.
Published: (2024)
by: Ji, Yixin, et al.
Published: (2024)
COPAL: Continual Pruning in Large Language Generative Models
by: Malla, Srikanth, et al.
Published: (2024)
by: Malla, Srikanth, et al.
Published: (2024)
PAT: Pruning-Aware Tuning for Large Language Models
by: Liu, Yijiang, et al.
Published: (2024)
by: Liu, Yijiang, et al.
Published: (2024)
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models
by: Hoffmann, David, et al.
Published: (2024)
by: Hoffmann, David, et al.
Published: (2024)
Pruning Large Language Models by Identifying and Preserving Functional Networks
by: Liu, Yiheng, et al.
Published: (2025)
by: Liu, Yiheng, et al.
Published: (2025)
Pruning as a Defense: Reducing Memorization in Large Language Models
by: Gupta, Mansi, et al.
Published: (2025)
by: Gupta, Mansi, et al.
Published: (2025)
Sample-aware Adaptive Structured Pruning for Large Language Models
by: Kong, Jun, et al.
Published: (2025)
by: Kong, Jun, et al.
Published: (2025)
Adaptive Pruning for Large Language Models with Structural Importance Awareness
by: Zheng, Haotian, et al.
Published: (2024)
by: Zheng, Haotian, et al.
Published: (2024)
A Simple and Effective Pruning Approach for Large Language Models
by: Sun, Mingjie, et al.
Published: (2023)
by: Sun, Mingjie, et al.
Published: (2023)
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
by: Demidovich, Yury, et al.
Published: (2024)
by: Demidovich, Yury, et al.
Published: (2024)
SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models
by: Zhu, Hourun, et al.
Published: (2025)
by: Zhu, Hourun, et al.
Published: (2025)
Beyond One-Way Pruning: Bidirectional Pruning-Regrowth for Extreme Accuracy-Sparsity Tradeoff
by: Liu, Junchen, et al.
Published: (2025)
by: Liu, Junchen, et al.
Published: (2025)
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
by: Guo, Kai, et al.
Published: (2024)
by: Guo, Kai, et al.
Published: (2024)
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models
by: Wang, Ziyan, et al.
Published: (2025)
by: Wang, Ziyan, et al.
Published: (2025)
Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
by: Das, Rocktim Jyoti, et al.
Published: (2023)
by: Das, Rocktim Jyoti, et al.
Published: (2023)
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
by: Xu, Peng, et al.
Published: (2024)
by: Xu, Peng, et al.
Published: (2024)
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
by: Yi, Kai, et al.
Published: (2024)
by: Yi, Kai, et al.
Published: (2024)
MAST: Model-Agnostic Sparsified Training
by: Demidovich, Yury, et al.
Published: (2023)
by: Demidovich, Yury, et al.
Published: (2023)
Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models
by: Xu, Zihuai, et al.
Published: (2025)
by: Xu, Zihuai, et al.
Published: (2025)
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
by: Li, Yixiao, et al.
Published: (2025)
by: Li, Yixiao, et al.
Published: (2025)
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
by: Ao, Shuang, et al.
Published: (2025)
by: Ao, Shuang, et al.
Published: (2025)
Sparsest Models Elude Pruning: An Exposé of Pruning's Current Capabilities
by: Zhang, Stephen, et al.
Published: (2024)
by: Zhang, Stephen, et al.
Published: (2024)
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
by: Wang, Ziyan, et al.
Published: (2025)
by: Wang, Ziyan, et al.
Published: (2025)
Sink-Aware Pruning for Diffusion Language Models
by: Myrzakhan, Aidar, et al.
Published: (2026)
by: Myrzakhan, Aidar, et al.
Published: (2026)
Doubly Robust Alignment for Large Language Models
by: Xu, Erhan, et al.
Published: (2025)
by: Xu, Erhan, et al.
Published: (2025)
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)
by: Lu, Xudong, et al.
Published: (2024)
Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy
by: Kim, Minsang, et al.
Published: (2024)
by: Kim, Minsang, et al.
Published: (2024)
Beam Prediction based on Large Language Models
by: Sheng, Yucheng, et al.
Published: (2024)
by: Sheng, Yucheng, et al.
Published: (2024)
Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models
by: Hu, Wentao, et al.
Published: (2025)
by: Hu, Wentao, et al.
Published: (2025)
Similar Items
-
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
by: Ilin, Ivan, et al.
Published: (2025) -
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
by: Yi, Kai, et al.
Published: (2024) -
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
by: Kang, Yuhan, et al.
Published: (2025) -
Exploring Federated Pruning for Large Language Models
by: Guo, Pengxin, et al.
Published: (2025) -
Large Language Model Pruning
by: Huang, Hanjuan, et al.
Published: (2024)