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New StoSignSGD algorithm fixes SignSGD's convergence problems for training large language models on non-smooth objectives
arXiv cs.LG · April 20, 2026
AI Summary
- •SignSGD has been popular for distributed learning and foundation model training but fails to converge on non-smooth objectives common in modern ML (ReLUs, max-pools, mixture-of-experts)
- •StoSignSGD introduces structural stochasticity into the sign operator while keeping updates unbiased, solving SignSGD's fundamental convergence limitations
- •Theoretical analysis proves StoSignSGD achieves sharp convergence rates matching lower bounds in convex optimization and improves performance in challenging non-convex non-smooth settings
- •The algorithm maintains the computational efficiency benefits of sign-based methods while extending applicability to modern neural network architectures
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