arXiv Computer Vision
Sep 25

ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation

ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.

By Zichong Meng, Chongjian Ge, Chun-Hao P. Huang, Yang Zhou, Huaizu Jiang
arXiv AI
Aug 11

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

arXiv:2608. 09226v1 Announce Type: cross Abstract: Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression.

By Yuhan Li, Fangao Zeng, Sicong Kang, Mengfei Xu, Hao Zhou, Wei Li, Pipei Huang, Bingbing Ni
Hugging Face Trending Papers
Aug 10

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression. We instead take an RL-native perspective: diffusion RL already generates reward-scored finite-step trajectories, whose intermediate states provide a natural source of distillation supervision rather than a disposable byproduct of sampling.

arXiv AI
4d ago

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

DMAD (Distribution Matching as Adversarial Distillation) reinterprets distribution matching as a classification task, enabling a few‑step student model to learn log‑density ratios directly via two discriminator heads on a shared backbone. This eliminates the need for an auxiliary diffusion model, reducing memory and computation overhead. Experiments show DMAD achieves state‑of‑the‑art Fréchet Inception Distance scores on ImageNet‑64x64, COCO‑10K, and VBench, and outperforms competing few‑step methods in joint audio‑video generation on MiniMax‑H3‑33B.

By Zhengming Yu, Junkun Yuan, Haotian Yang, Gordon Guocheng Qian, Yizhi Wang, Angtian Wang, Yiding Yang, Bo Liu, Xin Li, Wenping Wang, Chongyang Ma