Weight-Scale Growth Tracks Training Effort, Not Learning Quality

Does weight growth mean learning? Previous work showed that data predictability forecasts growth in the Weibull weight-scale parameter λ during transformer training. We test what this growth records, why it can diverge from generalization, and how it accumulates. In a controlled factorial grid crossing token shuffling with fixed-budget repetition, fully shuffled, highly repeated data produces the largest Δλ² but the worst held-out loss. Under fixed AdamW, Δλ² closely tracks accumulated radial-alignment work. A component- and time-resolved follow-up further shows that data conditions change both when this effort accumulates and whether it is routed toward Transmission or query/key Selection weights. Within the main grid, pairing Δλ² with the train–held generalization gap separates learning, memorization, and nothing-to-learn without seed-level label flips. The same frozen thresholds preserve this separation under matched corner interventions on C4 and Python code. These experiments trace weight-scale growth from controlled data construction through optimizer work and component-resolved weight evolution to behavior. Weight-scale growth therefore describes training activity, but alone does not determine whether the fitted structure generalizes.