arXiv:2606.14259v1 Announce Type: new Abstract: Prior work has identified several factors that can contribute to the performance gap between Adam and SGD, spanning data aspects, architecture design, and optimization properties. Yet these explanations are often studied in isolation, leaving their relative importance unclear. In this work, we revisit these hypotheses through a controlled empirical study across vision, language, genomics, and graph tasks, spanning modern and classical architectures, and carefully designed training setups. Our results suggest that no single factor consistently exp
The proliferation of AI models across diverse applications makes understanding and optimizing their training increasingly critical.
Improving our understanding of AI optimization techniques directly impacts the efficiency, performance, and accessibility of AI development across industries.
This research refines our long-held understanding of the differences between Adam and SGD optimizers, potentially leading to more targeted and effective AI training strategies.
- · AI researchers
- · Machine learning engineers
- · AI-driven product developers
More efficient and reliable training of complex AI models becomes possible.
Reduced computational costs and shorter development cycles for new AI applications could emerge.
Increased accessibility to advanced AI capabilities for a broader range of organizations due to lower barriers to entry.
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