arXiv AI By Seungmin Oh, Donggeon Lee, Jongbin Ryu

Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning

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arXiv Computation and Language
Aug 31

Pruning Laws for Large Language Models

arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly gro...

By Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
arXiv AI
Jul 16

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

arXiv:2607. 13124v1 Announce Type: cross Abstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires.

By Qingyu Zhang, Qianhao Yuan, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Xiang Li, Ming Xu, Jiarui Li, Xiuyin Zhao
arXiv AI
Aug 25

Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

The paper revisits the impact of pruning on large language models (LLMs) during test-time scaling (TTS). While prior work found that structured pruning degrades reasoning performance, this study shows that unstructured pruning—removing only specific redundant weights—can actually improve TTS performance on reasoning benchmarks for models s1.1-7B and Qwen3-8B, sometimes surpassing the full-weight models. The authors also examine how different layer-wise sparsity allocation strategies affect these outcomes.

By Ocean Monjur, Shahriar Kabir Nahin, Anshuman Chhabra
arXiv Machine Learning
Jun 9

Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy

arXiv:2606. 09080v1 Announce Type: new Abstract: Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers, heads, dimensions, and attention patterns.

By Haozhe Hu, Hao Wu, Anhao Zhao, Longwei Ding, Peiran Yin, Yunpu Ma, Xiaoyu Shen