arXiv:2605.31371v1 Announce Type: new Abstract: Sign-based and LMO-inspired optimizers have recently attracted substantial attention in deep learning due to their strong performance and low memory footprint. However, their fixed-magnitude updates can hurt terminal convergence: they decouple update mechanisms from gradient magnitudes and fail to account for parameter heterogeneity, often leading to oscillation rather than convergence. We propose SoftSignum, a smooth relaxation of sign-based optimization that replaces the hard sign map with a temperature-controlled soft-sign transformation, enab
This development emerges as deep learning research continues to push efficiency and performance boundaries in optimizer design, a critical component for AI training.
Improving optimizer performance, particularly by addressing parameter heterogeneity and convergence issues, directly impacts the efficiency and scalability of AI model development and deployment.
Optimizers could become more robust and less prone to oscillation, leading to faster and more stable training of complex deep learning models.
- · AI researchers
- · Deep learning practitioners
- · Companies with large AI training needs
- · Cloud AI providers
- · Hardware designers optimized solely for current optimizer paradigms
More efficient and stable training of deep learning models.
Reduced computational costs and shorter development cycles for AI breakthroughs across various applications.
Potentially enables the training of even larger, more complex AI models previously constrained by optimization limitations.
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Read at arXiv cs.LG