Path-Guided Flow Matching for Dataset Distillation

Dataset distillation compresses large datasets into compact synthetic sets with comparable performance in training models. Despite recent progress on diffusion-based distillation, such methods typically rely on heuristic guidance or prototype assignment over long denoising chains, which increases sampling cost and makes prototype-consistent control harder under strong guidance or low IPC. We propose \emph{Path-Guided Flow Matching (PGFM)}, the first flow matching-based framework for generative distillation, which enables deterministic synthesis by solving an ODE in a few steps. In particular, we introduce a retrieval-based prototype inversion stage that identifies prototype-consistent initial noises for class prototypes on the frozen flow manifold, and further develop an anchor-guided residual correction strategy for bounded stage-wise control. This design follows a controlled-transport principle: retrieval reduces initialization mismatch, while anchor guidance provides a bounded residual correction along the flow trajectory. Extensive experiments across high-resolution benchmarks demonstrate that PGFM matches or surpasses prior diffusion-based distillation approaches with fewer sampling steps while delivering competitive performance with improved efficiency.