OOPS: Outlier-aware and quadratic programming based structured pruning for large language models

doi:10.1016/j.neunet.2025.108332

OOPS: Outlier-aware and quadratic programming based structured pruning for large language models

2025-11-25

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DOI：10.1016/j.neunet.2025.108332

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The large model size and resource consumption of Large Language Models (LLMs) limit their deployment and application in many scenarios. Structured pruning offers a solution to this challenge. Based on the need for retraining after pruning, structured pruning methods for LLMs fall into two categories: retraining-free and retraining-based. Retraining-free methods often result in significant performance degradation, while retraining-based methods may require substantial computational resources. To address these limitations, we propose a structured pruning framework named OOPS (Outlier-aware and quadratic prOgramming based Structured Pruning). It comprises three key components: outlier-aware pruning unit selection, quadratic programming based reconstruction, and layer-wise distillation. By employing the first two components, OOPS prunes models without the requirement of retraining, outperforming existing retraining-free methods. When further incorporating layer-wise distillation to train the pruned layers individually, OOPS surpasses other retraining-based methods with lower computational costs. We evaluate the effectiveness of OOPS on 11 models from 4 LLM families across multiple tasks, demonstrating its superior performance compared to state-of-the-art methods in both retraining-free and retraining-based settings.

OOPS: Outlier-aware and quadratic programming based structured pruning for large language models

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