Reservoir of Importance: Learning Semi-Structured Sparsity with Differentiable Subset Sampling
Semi-structured $N$:$M$ sparsity has emerged as a practical direction for accelerating large language models (LLMs). However, existing learnable-mask approaches incur substantial parameter and memory overhead, limiting their scalability to large models and aggressive sparsity regimes. In this work, we revisit semi-structured pruning from a perspective that reconciles efficiency with scalability. We propose Reservoir of Importance (RoI), a lightweight semi-structured pruning framework that learns sparsity masks through differentiable subset sampling. Unlike prior methods that model full categorical distributions over all feasible $N$:$M$ patterns, RoI introduces a compact-logit parameteriz...
| Authors | Ha Dinh, Xuan Duy Ta, Khoat Than et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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