In the context of Bayesian inference, recent advances in coreset methods have shown that careful selection of representative datapoints can replace massive volumes of data, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points…