# SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training Source: [SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training]() Collection index: [llms.txt]() Data updated: 2026-05-29T05:00:05.519Z Authors: Mohammed Adnan, Rohan Jain, Tom Jacobs, Ekansh Sharma, Rahul G. Krishnan, Rebekka Burkholz, Yani Ioannou Published: 2026-05-26T18:14:19.000Z ## Abstract Dynamic Sparse Training (DST) methods train neural networks by maintaining sparsity while dynamically adapting the network topology. Despite the promise of reduced computation, DST methods converge significantly slower than dense training, often requiring comparable training time to achieve similar accuracy. We demonstrate both analytically and empirically that Batch Normalization (BN) adversely affects sparse training, and propose SparseOpt, a sparsity-aware optimizer, to address this. Experiments on ResNet models across CIFAR-100 and ImageNet demonstrate consistently faster convergence and improved generalization with our proposed method. Our work highlights the limitations of current normalization layers in sparse training and provides the first systematic study of the interaction between Batch Normalization, sparse layers, and DST, taking a significant step toward making DST practically competitive with dense training. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet ILSVRC-2012 (val)](): Image Classification; ImageNet ILSVRC-2012 (val); Top-1 Accuracy: 75.26. ## Original sources and implementation [Original paper]() [PDF]()