SLORR Adds Stateless In-Training Low-Rank Regularization Without SVDs

arXiv·medium signal

Low-rank factorization compresses neural networks, but modern models resist aggressive factorization without accuracy loss, and existing training-time low-rank regularizers require SVDs of large weight matrices, modify architecture with extra trainable parameters, or depend on stateful cached quantities. SLORR (arXiv 2607.08754, July 9) is simple and stateless, avoiding all three costs. For practitioners doing their own fine-tunes, this is a low-friction way to make a model compressible later without paying SVD cost during training.