Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity

ArXi:2510.00304v3 Announce Type: replace Deep learning models excel in stationary data but struggle in non-stationary environments due to a phenomenon known as loss of plasticity (LoP), the degradation of their ability to learn in the future. This work presents a first-principles investigation of LoP in gradient-based learning. Grounded in dynamical systems theory, we formally define LoP by identifying stable manifolds in the parameter space that trap gradient trajectories.