Beyond Faster Fields: Auditing Drifting Generative Models

Drifting generative models train a generator through an attraction–repulsion field that moves samples toward a target distribution. We ask whether cheaper fields preserve the intended update, reduce full training cost, and maintain distributional fidelity. We compare optimized dense drifting with projected, representative-based, and transport alternatives in a 240-run feature benchmark, a 24-run image screen, and a 66-model generator–encoder audit. An exact aggregation identity shows how to preserve normalization and copy-specific self deletion when compressing repeated sources. Compatible operator references change apparent approximation error. A 9.26× field saving yields only 1.013× inclusive wall speedup in the balanced image screen. At 15k updates in the balanced small-generator/ResNet-18 bridge, DriftXpress uses about 26.7% less recorded training-component time, with lower FIDtv and higher recall in all three paired seeds under both tested Inception input paths; earlier recall gains are input-sensitive. All twelve rare-target screen runs have zero undercoverage, yet classifier-estimated rare-mass means are 5.6–9.9× the target. These results locate a positive checkpoint regime without establishing general transfer or exact matched-quality speedup. The practical takeaway is to compare native operators, declare cost scope, and measure quality, diversity, and two-sided mass error.