EPFL's Lan Feng, Wuyang Li, and Alexandre Alahi, in arXiv 2607.02375v1 released on July 2, let the classic MMD objective function — which was pronounced "unable to train a generator" just a year ago — reclaim the crown on ImageNet single-step image generation. This Improved Representation Distribution Matching (iRDM) digs deeper into the "distribution matching" paradigm that FLUX and SDXL have overused — after systematically sorting out two design axes (how to compare distributions, in which representation to compare), the authors arrive at three counter-intuitive conclusions: (1) the classic MMD, as long as the estimation method is right, regains scalability; (2) the generated batch size is the key variable, with the optimal value needing to exceed 2048, far beyond the conventional batch; (3) any single representation can be "score-gamed", so a balanced set of encoders must be used for comparison, and the authors propose SW_r14 — a 14-encoder Sliced-Wasserstein-based evaluation metric, specifically to prevent cheating. The most explosive are the deployment numbers: iRDM posts SW_r14 1.30 SOTA on ImageNet single-step generation; in PickScore human preference testing, 71.2% of samples are judged by users to be better than the previous best single-step model. Applying the method to FLUX.2 [klein] 4-step version, with only 90 H200 GPU-hours, post-trains it into a single-step generator — GenEval improves from 0.794 to 0.826, PickScore from 22.58 to 22.76, single-step overtaking four-step. My judgment: the MMD revival is yet another "old method rising again" in deep-learning history — the potential of core objective functions may be more important than new architectures; FLUX.2 [klein] 90 GPU-hours compressed to single-step means single-step generation is officially qualified for "production" — real-time interaction, content platforms, and ad creative pipelines will all follow; project page + SW_r14 are both open-sourced, with very low community reproduction threshold. The next wave of key questions: can iRDM be applied to video generation? Does SW_r14 still prevent cheating beyond text-to-image? These answers may determine the direction of diffusion model distillation in the second half of 2026. Source: arXiv:2607.02375.