@inproceedings{yang-etal-2025-wanda,
title = "Wanda++: Pruning Large Language Models via Regional Gradients",
author = {Yang, Yifan and
Zhen, Kai and
Ganesh, Bhavana and
Galstyan, Aram and
Huybrechts, Goeric and
M{\"u}ller, Markus and
K{\"u}bler, Jonas M. and
Swaminathan, Rupak Vignesh and
Mouchtaris, Athanasios and
Bodapati, Sravan Babu and
Susanj, Nathan and
Zhang, Zheng and
FitzGerald, Jack and
Kumar, Abhishek},
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.224/",
doi = "10.18653/v1/2025.findings-acl.224",
pages = "4321--4333",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy degradation without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that outperforms the state-of-the-art methods by utilizing decoder-block-level \textbf{regional} gradients. Specifically, Wanda++ improves the pruning score with regional gradients for the first time and proposes an efficient regional optimization method to minimize pruning-induced output discrepancies between the dense and sparse decoder output. Notably, Wanda++ improves perplexity by up to 32{\%} over Wanda in the language modeling task and generalizes effectively to downstream tasks. Moreover, despite updating weights with regional optimization, Wanda++ remains orthogonal to sparsity-aware fine-tuning, further reducing perplexity with LoRA in great extend. Our approach is lightweight, pruning a 7B LLaMA model in under 10 minutes on a single H100 GPU."
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<abstract>Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy degradation without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that outperforms the state-of-the-art methods by utilizing decoder-block-level regional gradients. Specifically, Wanda++ improves the pruning score with regional gradients for the first time and proposes an efficient regional optimization method to minimize pruning-induced output discrepancies between the dense and sparse decoder output. Notably, Wanda++ improves perplexity by up to 32% over Wanda in the language modeling task and generalizes effectively to downstream tasks. Moreover, despite updating weights with regional optimization, Wanda++ remains orthogonal to sparsity-aware fine-tuning, further reducing perplexity with LoRA in great extend. Our approach is lightweight, pruning a 7B LLaMA model in under 10 minutes on a single H100 GPU.</abstract>
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%0 Conference Proceedings
%T Wanda++: Pruning Large Language Models via Regional Gradients
%A Yang, Yifan
%A Zhen, Kai
%A Ganesh, Bhavana
%A Galstyan, Aram
%A Huybrechts, Goeric
%A Müller, Markus
%A Kübler, Jonas M.
%A Swaminathan, Rupak Vignesh
%A Mouchtaris, Athanasios
%A Bodapati, Sravan Babu
%A Susanj, Nathan
%A Zhang, Zheng
%A FitzGerald, Jack
%A Kumar, Abhishek
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yang-etal-2025-wanda
%X Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy degradation without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that outperforms the state-of-the-art methods by utilizing decoder-block-level regional gradients. Specifically, Wanda++ improves the pruning score with regional gradients for the first time and proposes an efficient regional optimization method to minimize pruning-induced output discrepancies between the dense and sparse decoder output. Notably, Wanda++ improves perplexity by up to 32% over Wanda in the language modeling task and generalizes effectively to downstream tasks. Moreover, despite updating weights with regional optimization, Wanda++ remains orthogonal to sparsity-aware fine-tuning, further reducing perplexity with LoRA in great extend. Our approach is lightweight, pruning a 7B LLaMA model in under 10 minutes on a single H100 GPU.
%R 10.18653/v1/2025.findings-acl.224
%U https://aclanthology.org/2025.findings-acl.224/
%U https://doi.org/10.18653/v1/2025.findings-acl.224
%P 4321-4333
Markdown (Informal)
[Wanda++: Pruning Large Language Models via Regional Gradients](https://aclanthology.org/2025.findings-acl.224/) (Yang et al., Findings 2025)
ACL
- Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, and Abhishek Kumar. 2025. Wanda++: Pruning Large Language Models via Regional Gradients. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4321–4333, Vienna, Austria. Association for Computational Linguistics.