Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-reasoning setting (Qwen3-8B-Base, with a 4B replication; five semantically rule-disjoint KOR-Bench domains) we train 30 allocations spanning the five-domain simplex, 24 sweep configurations plus six withheld from the fit, at five seeds each. Three findings emerge. First, every domain has an interior coverage optimum: the moderate band (
10%-
40%) is best for all five domains, and a calibrated permutation test for quadratic interiority gives
P≈0.010; the fitted mid-training-only curves, with 8B peaks between
9.9% and
35.1%, reproduce for curve shape but not peak location. Second, the gaps survive a fixed-budget alignment pass: compensatory SFT raises 116/120 cells (mean
+4.32%) yet bridges
0/240 pairs at a
5% threshold and
30/240 at a
10% ratio, an equal-budget uniform control behaves almost identically, and a permutation null would bridge
13.8±3.3 and
77.9±8.5 pairs (
P<0.001). Third, zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is commingled with generic drift. An exploratory
θ∗ allocation attains the largest full-pipeline gain (
+4.36% vs.
+0.80%/
+0.64%,pp) but is marginal under Welch test.