AI RESEARCH

Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos

arXiv CS.LG

ArXi:2605.21648v1 Announce Type: new We develop a mean-field theory of dropout as a perturbation of critical signal propagation at the edge of chaos. Dropout shifts the perfect-alignment fixed point, making the depth scale for information propagation finite even at critical initialization. We derive critical and crossover scaling laws for correlation decay and establish that smooth activations and kinked, ReLU-like activations constitute distinct universality classes, with different critical exponents and a universal two-parameter scaling collapse in detuning and dropout strength.