LSAM: Asynchronous Distributed Training with Landscape-Smoothed Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2509.03110v1
• Date: Wed, 03 Sep 2025 08:07:43 GMT
• Title: LSAM: Asynchronous Distributed Training with Landscape-Smoothed Sharpness-Aware Minimization
• Authors: Yunfei Teng, Sixin Zhang,
• Abstract summary: Sharpness-Aware Minimization (SAM) improves generalization in deep neural networks by minimizing both loss and sharpness.<n>We present Landscape-Smoothed SAM (LSAM), a novel generalization that preserves SAM's advantages while offering superior efficiency.
• Score: 6.794145254474338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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