3BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs
Sparse plus Low-Rank $(\mathbf{S} + \mathbf{LR})$ decomposition of Large Language Models (LLMs) has emerged as a promising direction in model compression, aiming to decompose pre-trained model weights into a sum of sparse and low-rank matrices $(\mathbf{W} \approx \mathbf{S} + \mathbf{LR})$. Despite recent progress, existing methods often suffer from substantial performance degradation compared to dense models. In this work, we introduce 3BASiL-TM, an efficient one-shot post-training method for $(\mathbf{S} + \mathbf{LR})$ decomposition of LLMs that addresses this gap. Our approach first introduces a novel 3-Block Alternating Direction Method of Multipliers (ADMM) method, termed 3BASiL, t...
| Authors | Mehdi Makni, Xiang Meng, Rahul Mazumder |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 8 October 2026 |
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