Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of \emph{when} it occurs in hyperparameter space remains uncharacterized. We map the memorization-to-generalization boundary across 384 configurations of two-hidden-layer MLPs on modular arithmetic, fitting a power-law scaling relation for generalization onset time:
Tgrok∝H−0.27D−2.04η−0.50λ−0.64 (
R2=0.732;
0.821 with interactions). The exponent hierarchy reveals that data complexity (
D−2.04) is the dominant driver of regime transition, not model capacity (
H−0.27): doubling data accelerates generalization by
∼4×, while doubling width yields only
∼1.2×. A sharp phase boundary at weight decay
λ≳1.0 separates grokking from non-grokking configurations, and weight norm trajectories show monotonic compression during the transition, consistent with implicit regularization selecting low-complexity solutions. These results provide a quantitative foundation for predicting and controlling regime transitions in overparameterized networks.