Progressive Binarization with Semi-Structured Pruning for LLMs

• URL: http://arxiv.org/abs/2502.01705v4
• Date: Sat, 27 Sep 2025 03:38:26 GMT
• Title: Progressive Binarization with Semi-Structured Pruning for LLMs
• Authors: Xianglong Yan, Tianao Zhang, Zhiteng Li, Haotong Qin, Yulun Zhang,
• Abstract summary: We propose Progressive Binarization with Semi-Structured Pruning (PBS$2$P), a novel post-training framework that seamlessly integrates binarization and semi-structured pruning.<n>We show that PBS$2$P consistently outperforms state-of-the-art (SOTA) binary post-training quantization methods in both perplexity and downstream accuracy.
• Score: 36.91249209658632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have achieved remarkable progress in natural language processing, but their high computational and memory costs hinder deployment on resource-constrained devices. Binarization represents the most extreme form of quantization, yet binarized models still contain redundancy that can be further removed. Pruning provides a natural way to eliminate such redundancy, but na\"ive combination with binarization often results in severe performance degradation. In this paper, we propose Progressive Binarization with Semi-Structured Pruning (PBS$^2$P), a novel post-training framework that seamlessly integrates binarization and semi-structured pruning. We first propose Stepwise semi-structured Pruning with Binarization Optimization (SPBO), which progressively introduces sparsity while optimizing binarization parameters to jointly reduce pruning and quantization error, yielding more stable and accurate compression. Additionally, we propose a Coarse-to-Fine Search (CFS) that first allocates pruning ratios and then refines element selection, further enhancing overall performance. Extensive experiments across multiple LLM families show that PBS$^2$P consistently outperforms state-of-the-art (SOTA) binary post-training quantization methods in both perplexity and downstream accuracy. The code and models will be available at https://github.com/XIANGLONGYAN/PBS2P.

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