[2510.17991] Demystifying Transition Matching: When and Why It Can Beat Flow Matching

Computer Science > Machine Learning arXiv:2510.17991 (cs) [Submitted on 20 Oct 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:Demystifying Transition Matching: When and Why It Can Beat Flow Matching Authors:Jaihoon Kim, Rajarshi Saha, Minhyuk Sung, Youngsuk Park View a PDF of the paper titled Demystifying Transition Matching: When and Why It Can Beat Flow Matching, by Jaihoon Kim and 3 other authors View PDF HTML (experimental) Abstract:Flow Matching (FM) underpins many state-of-the-art generative models, yet recent results indicate that Transition Matching (TM) can achieve higher quality with fewer sampling steps. This work answers the question of when and why TM outperforms FM. First, when the target is a unimodal Gaussian distribution, we prove that TM attains strictly lower KL divergence than FM for finite number of steps. The improvement arises from stochastic difference latent updates in TM, which preserve target covariance that deterministic FM underestimates. We then characterize convergence rates, showing that TM achieves faster convergence than FM under a fixed compute budget, establishing its advantage in the unimodal Gaussian setting. Second, we extend the analysis to Gaussian mixtures and identify local-unimodality regimes in which the sampling dynamics approximate the unimodal case, where TM can outperform FM. The approximation error decreases as the minimal distance between component means increases, highlighting that TM is favored when the mod...