Neural architecture search (NAS) evaluates candidate networks, but fully training enough architectures to rank an entire space is expensive.
Zero-cost proxies score architectures at initialization, yet their ranking quality varies across search spaces.
Learned predictors reduce evaluation cost but typically require fully trained labels or partial-training features for individual candidates.
We introduce $\textbf{RiPPLE}$, $\underline{\textbf{R}}$anking v$\underline{\textbf{i}}$a $\underline{\textbf{P}}$refix-$\underline{\textbf{P}}$ropagated $\underline{\textbf{L}}$abel $\underline{\textbf{E}}$xtrapolation, which treats partial training as a source of labels for a small coverage set of anchors.
RiPPLE trains these anchors to an early prefix, extrapolates their learning curves to surrogate labels, and propagates the labels over label-free architecture features.
The early-training signal remains a label on the anchors rather than a per-candidate feature.
Feature, readout, and encoding rules are selected without held-out accuracy and reused across search spaces.
We evaluate the method on twelve benchmark cells from four search-space families and on the larger DARTS space.
The results examine ranking quality, label efficiency, architecture selection, and the roles of readout, coverage, and propagation.
RiPPLE provides a whole-space ranking from a fractional anchor-training budget, with comparisons interpreted under their respective evaluation and cost protocols.