VRAdam hybridizes Adam's per-parameter adaptation with a physics-inspired velocity regularizer to stabilize training at the edge of stability, delivering better empirical performance than AdamW and O(ln(N)/sqrt(N)) convergence bounds under mild assumptions.
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A Physics-Inspired Optimizer: Velocity Regularized Adam
VRAdam hybridizes Adam's per-parameter adaptation with a physics-inspired velocity regularizer to stabilize training at the edge of stability, delivering better empirical performance than AdamW and O(ln(N)/sqrt(N)) convergence bounds under mild assumptions.