SLORR: Simple and Efficient In-Training Low-Rank Regularization
Score
78提出了新的无状态低秩正则化框架，填补了现有方法在简洁性和低开销方面的空白。
方法设计清晰，有理论近似保证，实验覆盖多种规模和任务，但缺少与其他训练时正则化方法的直接对比。
训练开销极低（<1%～8%），且无需修改模型架构，易于集成到现有训练流程中。
论文发布于2025年7月，紧贴大模型压缩和高效训练的前沿需求。
源自arXiv预印本，尚未经同行评审，但作者来自知名机构且方法有理论支撑。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents a simple yet effective low-rank regularization framework with theoretical analysis and extensive experiments, a solid contribution to model compression.
AI commentary
「The strength of this paper lies in its simplicity and efficiency—it can be integrated into existing training pipelines without complex modifications. For researchers working on model compression and edge deployment, SLORR offers a low-cost way to improve compressibility. Particularly noteworthy is its performance in LLM pretraining with less than 1% overhead, which is highly attractive for real-world large-scale training.」
Quick overview
SLORR is a stateless, architecture-preserving low-rank regularization framework instantiated with variants based on the Hoyer sparsity metric and the nuclear norm. It directly regularizes original weight matrices using GPU-friendly approximations, inducing compressibility with less than 8% training overhead on ImageNet-1K experiments (ResNet-50, ViT) and less than 1% overhead in LLM pretraining at 135M and 560M scales, while preserving significantly better compressed model performance than unregularized models.
- SLORR introduces two variants (Hoyer sparsity and nuclear norm) with no SVD, extra parameters, or architecture modifications.
- GPU-friendly forward and backward approximations incur less than 1%–8% training overhead with approximation guarantees.
- Validated on image classification and LLM pretraining, compressed models suffer substantially less performance degradation than unregularized baselines.
Full text
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