arXiv:2505.13196v3 Announce Type: replace Abstract: We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quartic terms for kinetic energy with its stabilizing effects on various system dynamics. Previous algorithms, including the ubiquitous Adam, operate at the so-called adaptive edge of stability regime during training, leading to rapid oscillations and slowed convergence of loss. However, VRAdam adds a higher order penalty on the learning rate based on the velocity such that the algorithm automatically slows
The continuous drive for more efficient and stable deep learning training algorithms has led researchers to explore novel approaches, including physics-inspired optimizations, as current methods like Adam face limitations.
Improved optimization algorithms directly enhance the efficiency, stability, and speed of AI model training, accelerating AI development and deployment across various industries.
The introduction of VRAdam offers a new optimization paradigm that could lead to more stable and faster convergence for deep learning models, potentially reducing computational costs and training times.
- · AI developers and researchers
- · Cloud AI infrastructure providers
- · Sectors heavily reliant on deep learning
- · Hardware manufacturers for AI
- · Existing less efficient optimization algorithms
- · Organizations with legacy AI training pipelines
Deep learning models will train faster and more reliably, leading to quicker iteration cycles for AI development.
Reduced training times and computational requirements could lower the barriers to entry for AI development and democratize access to advanced AI capabilities.
More sophisticated and stable AI systems could emerge faster, potentially accelerating progress in autonomous agents and complex decision-making AI.
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Read at arXiv cs.LG