arXiv Machine Learning
Jul 21

NIRVANA: Structured Pruning Reimagined for Large Language Model Compression

arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.

By Mengting Ai, Tianxin Wei, Sirui Chen, Jingrui He
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
Aug 10

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608. 06901v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments.

By Minseok Kang, Hyunwoo Kim, Chanyoung Kim, Minwoo Kim, Jaekoo Lee, Dahuin Jung