While Sharpness-Aware Minimization (SAM) improves generalization in deep neural networks by minimizing both loss and sharpness, it suffers from inefficiency in distributed large-batch training. We present Landscape-Smoothed SAM (LSAM), a novel optimizer that preserves SAM's generalization advantages while offering superior efficiency. LSAM integrates SAM's adversarial steps with an asynchronous distributed sampling strategy, generating an asynchronous distributed sampling scheme, producing a smoothed sharpness-aware loss landscape for optimization. This design eliminates synchronization bottlenecks, accelerates large-batch convergence, and delivers higher final accuracy compared to data-parallel SAM.
@article{arxiv.2509.03110,
title = {LSAM: Asynchronous Distributed Training with Landscape-Smoothed Sharpness-Aware Minimization},
author = {Yunfei Teng and Sixin Zhang},
journal= {arXiv preprint arXiv:2509.03110},
year = {2025}
}