Scout-Refine-Correct (SRC) is a training recipe that equips flow-matching image generation models with an inference-time refinement capability. During sampling, the model performs a scout rollout to predict a full sample, uses this prediction as a self-conditioning signal, and then takes the actual step; this process can be repeated for multiple refinement rounds. On ImageNet-256, SRC improves LightningDiT-B’s FID from ~14.5 to ~7.26 with 550 normalised flow evaluations, matching a best-of‑N baseline that relies on an oracle verifier, and further reduces FID to 3.81 when combined with best-of‑N.
By Sergey Karpukhin, Aibek Alanov
arXiv:2512. 19311v2 Announce Type: replace-cross Abstract: This paper studies the training-testing discrepancy (a.
By Hui Li, Fu-Yun Wang, Haoyuan Xia, Jiayue Lyu, Kaihui Cheng, Siyu Zhu, Jingdong Wang
Preconditioned Flow Matching introduces a precondition-then-match framework that transforms target distributions into more isotropic representations before training flow matching models. By reshaping intermediate distributions, the method improves conditioning, leading to better low-eigenvalue recovery and enhanced performance metrics such as FID, MMD, precision, and recall across Gaussian, Gaussian-mixture, and high-resolution image datasets. The approach is theoretically justified and empirically validated, with ablations showing gains stem from improved geometry rather than extra parameters.
By Shadab Ahamed, Eshed Gal, Md Shahriar Rahim Siddiqui, Simon Ghyselincks, Moshe Eliasof, Eldad Haber
The study investigates whether the usefulness of training samples depends on the data itself or on the learner. By fixing selected subsets and varying the learner, the authors show that the crossover point—where easy samples give way to coverage samples—shifts with changes in model width, input resolution, and architecture. This indicates that the boundary between easy and coverage samples is learner‑dependent rather than data‑dependent.
By Yangze Liu, Xiao-Long Yin, Zhongyi Han
The paper introduces pass@k, a metric that evaluates how well generative diffusion models cover their output distribution by measuring the probability that at least one of k independent samples meets a quality criterion. Using this metric, the authors show that while classifier‑free guidance improves single‑draw quality, its advantage diminishes or reverses as k increases, revealing a trade‑off between quality and distribution coverage. They further demonstrate that the choice of training objective in diffusion distillation—whether distribution‑matching or consistency/trajectory‑based—determines whether a few‑step model retains its teacher’s coverage or sacrifices it for higher single‑draw performance, a phenomenon that also appears in few‑step causal video generation.
By Yifei Wang, Xiaoyu Wu, Tsu-Jui Fu, Chen Chen, Liang-Chieh Chen, Zhe Gan, Chen Wei
arXiv:2608. 19540v1 Announce Type: new Abstract: Training fast generators on new domains with limited data remains challenging for two reasons.
By Yara Bahram, Zahra Dehghani, M\'elodie Desbos, Eric Granger, Pablo Piantanida, Mohammadhadi Shateri
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costly multi-step sampling unaddressed, and existing acceleration methods are tied to the source parameterization--$ε$, $x$, $v$, or $u$--leaving heterogeneous pretrained models with no common acceleration target.
arXiv:2608.19540v4 Announce Type: replace
Abstract: Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model...
By Yara Bahram, Zahra Dehghani, M\'elodie Desbos, Eric Granger, Pablo Piantanida, Mohammadhadi Shateri
arXiv:2606. 03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text.
By Bishwas Mandal, Shmuel Berman, Akshay Vegesna, Samip Dahal
arXiv:2603. 12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven.
By Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang
arXiv:2609.01244v1 Announce Type: new
Abstract: Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, whic...
By Charles O'Neill, Mudith Jayasekara, Harry Partridge
arXiv:2610.23531v1 Announce Type: new
Abstract: We introduce PONDER, a method that learns in which order an image generator should fill in content and which of its earlier predictions to revise. Mask...
By Julius Erbach, Dominik Narnhofer, Bernt Schiele, Jan Eric Lenssen, Konrad Schindler