#dynamical-isometry News & Analysis

Preserving Plasticity in Continual Learning via Dynamical Isometry

Researchers identify dynamical isometry—maintaining consistent layer-wise Jacobian singular values—as a mechanism for preserving neural network plasticity during continual learning under non-stationary conditions. They propose AdamO, an adaptive optimizer combining isometry regularization with gradient updates, demonstrating improved performance across supervised and reinforcement-learning benchmarks where traditional networks suffer progressive learning degradation.