Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

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.