AI RESEARCH
On The Hidden Biases of Flow Matching Samplers
arXiv CS.LG
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ArXi:2512.16768v3 Announce Type: replace-cross Flow matching (FM) constructs continuous-time ODE samplers by prescribing probability paths between a base distribution and a target distribution. In this note, we study FM through the lens of finite-sample plug-in estimation. In addition to replacing population expectations by sample averages, one may replace the target distribution itself by a finite-sample surrogate, ranging from the empirical measure to a smoothed estimator. This viewpoint yields a natural hierarchy of empirical FM models.