Physics-Inspired Optimizer VRAdam Improves Neural Network Training Efficiency
Researchers introduced Velocity-Regularized Adam (VRAdam), a new optimizer for training deep neural networks that applies physics-inspired velocity regularization to improve convergence. The method addresses oscillation problems in existing optimizers like Adam by automatically reducing learning rates when weight updates become large. The work demonstrates performance improvements across multiple tasks including image classification, language modeling, and generative modeling.
VRAdam is a novel optimization algorithm that combines physics-inspired principles with adaptive learning rate mechanisms to enhance deep neural network training. Drawing on quartic kinetic energy terms from physics, the optimizer adds a higher-order penalty based on velocity that dampens oscillations and slows convergence when weight updates become excessive. The researchers provide rigorous theoretical analysis showing convergence bounds of O(ln(N)/√N) for stochastic non-convex objectives under mild assumptions. Benchmarking across diverse architectures—including CNNs, Transformers, and GFlowNets—and tasks demonstrates that VRAdam outperforms standard optimizers like AdamW. The key innovation addresses the "edge of stability" regime where previous algorithms like Adam experience rapid oscillations that slow training.
What's missing
The paper does not discuss computational overhead or wall-clock time comparisons between VRAdam and baseline optimizers, which would be relevant for practical adoption. Additionally, the specific hyperparameter sensitivity of the velocity regularization term and guidance for practitioners on tuning this parameter across different problem domains is not detailed in the abstract.
What different sources said
- arXiv cs.AICenter
A Physics-Inspired Optimizer: Velocity Regularized Adam
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