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

Yunfei Teng; Sixin Zhang

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arXiv · 2509.03110

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

Yunfei Teng·Sixin Zhang

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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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Yunfei Teng, Sixin Zhang. 2025-09-03. LSAM: Asynchronous Distributed Training with Landscape-Smoothed Sharpness-Aware Minimization. https://arxiv.org/abs/2509.03110

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