ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix
  Factorization
Authors: name: Lawrence Liu, name: Alexander Liu, name: Mengdi Wang, name: Tuo Zhao, name: Lin F. Yang
Organization: University of California, Los Angeles

Large language models (LLMs) present significant deployment challenges due to
their immense computational and memory requirements. While semi-structured
pruning, particularly 2:4 sparsity, offers a path to practical hardware
acceleration, existing methods often incur substantial performance degradation.
To bridge this gap, we introduce ARMOR: (Adaptive Representation with
Matrix-factORization), a novel one-shot post-training pruning algorithm.
Instead of directly pruning weights, ARMOR factorizes each weight matrix into a
2:4 sparse core wrapped by two low-overhead, block diagonal matrices. These
wrappers act as efficient pre and post-transformation error correctors,
offering greater flexibility to preserve model quality compared to conventional
2:4 pruning techniques. The sparse core and block diagonal wrappers are chosen
through a block coordinate descent algorithm that minimizes a layer-wise proxy
loss. We theoretically prove this optimization is guaranteed to converge to a
solution with a proxy loss less than or equal to state-of-the-art pruning
algorithms. Experiments on Llama (Touvron et al., 2023; Dubey et al., 2024) and
Qwen (Yang et al., 2025) model families demonstrate that ARMOR consistently and
significantly outperforms state-of-the-art 2:4 pruning methods across a wide
range of downstream tasks and perplexity evaluations. ARMOR achieves this
superior performance while retaining the inference speedups and substantial
memory usage reductions of 2:4 pruning, establishing a more effective trade-off
between model compression and task accuracy