Sample-efficient pretraining under a developmental data budget, such as the ten million words and ten epochs of the BabyLM Strict-Small track, is usually approached by adding training signal: a new objective, optimizer, curriculum, or teacher. The budget itself receives less scrutiny. We show that much of it is spent after learning has stopped. A 33.0M-parameter GPT-BERT hybrid trained under the track rules saturates on downstream evaluations by roughly 70M words seen, about 70% of its permitted exposure, while training loss continues to fall; the final epochs improve fit, not competence. From this diagnosis we derive three design principles, timing, preservation, and alignment, and instantiate them as Minimal-Pair Preference Optimization (MPO): a short post-training phase, begun at the saturation checkpoint, that teaches the model to prefer real corpus sentences over length-preserving corruptions of them under a frozen-reference penalty. A controlled study of seven in-training interventions supports the principles: none improves on the baseline, and the unanchored ones damage the same cluster of long-distance dependencies first. MPO improves five of the seven official task averages, concedes under a point on the other two, is alone in repairing that cluster, and matches the fully trained baseline with 7.2 rather than 10 epochs.