Phase Transition from Random Features to Fully Trainable Networks

arXiv:2610.31663v1 Announce Type: cross Abstract: Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing. Partially trainable neural networks bridge fixed random features and fully adaptive representations. This paper quantifies a phase transition pattern in approximation bounds and observes analogous behavior in training errors: a rapid but smooth initial improvement followed by diminishing returns, with a sharper transition under greater initialization mismatch. Under polynomial spectral decay with exponent $\alpha>1$ and leverage compatibility, we establish a squared $L_2$ approximation rate $\mathcal{O}((\kappa_P\log(m/\delta)/m)^\alpha)$ for two-layer networks of width $m$ and trainable proportion $P$, where $\kappa_P=(P/\kappa_1^*+(1-P)/\kappa_0^*)^{-1}$. The bound holds uniformly over the unit ball of a reproducing kernel Hilbert space (RKHS) with probability at least $1-\delta$. The constants $\kappa_0^*$ and $\kappa_1^*$ quantify the fixed and trainable groups' sampling mismatches relative to the target RKHS. For spherical biased-ReLU features in dimension $d$, we verify these assumptions with $\alpha=(d+2)/(d-1)$ and $\kappa_0^*=4\sqrt{2}\tau(d+1)/d$ for initialization scale $\tau\ge1$, and $\kappa_1^*=1$ under ridge leverage sampling. For $d=5$ and $\tau=10$, a trainable proportion of approximately $4.1\%$ captures $90\%$ of the reduction in the approximation bound from $P=0$ to $P=1$. A structured three-layer example illustrates how a few trainable first-layer neurons exploit fixed downstream blocks through nonlinear composition. Numerical experiments across diverse settings, including networks with up to 15 layers, exhibit similar phase transitions in training errors.