Primal-Dual Flow Matching for Sample-Wise Constrained Generation
Primal-Dual Flow Matching (PDFM) is a method for learning data generation while enforcing specific sample-wise constraints. It uses a flow-matching parameterization to derive theoretical guarantees, requiring only simple binary membership feedback for the constraint set. This allows direct control over how well generated samples satisfy the constraint, offering superior performance compared to existing methods.