Ada2MS: A Hybrid Optimization Algorithm Based on Exponential Mixing of Elementwise and Global Second-Moment Estimates
arXiv:2605.20533v1 Announce Type: new Abstract: Optimization algorithms are core methods by which machine learning models iteratively minimize loss functions, update parameters, learn from data, and improve performance. Momentum SGD and AdamW represent two important optimization paradigms. AdamW produces stable updates and usually has strong robustness across training scenarios, but its generalization performance is sometimes weaker than that of momentum methods. Momentum SGD can often obtain better generalization after careful tuning, but it is more sensitive to gradient-scale variation and h
The continuous evolution of AI models necessitates more efficient and stable optimization algorithms to push performance boundaries, addressing existing trade-offs between AdamW's robustness and SGD's generalization capabilities.
Improved optimization algorithms directly translate to faster, more stable, and potentially more generalizable AI model training, impacting the development and deployment of advanced AI across various applications.
This new hybrid optimization method, Ada2MS, offers a potential path to combine the stability of AdamW with the generalization strength of momentum SGD, reducing the need for extensive hyperparameter tuning.
- · AI researchers and developers
- · Deep learning framework providers
- · Industries relying on large-scale AI models
More efficient training processes for complex neural networks.
Accelerated development of new AI applications and potentially more performant models in various domains.
A potential reduction in the computational resources and expertise required for optimizing cutting-edge AI, democratizing advanced AI development further.
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Read at arXiv cs.LG