arXiv Machine Learning
Jul 3

Optimizing Visual Generative Models via Distribution-wise Rewards

arXiv:2607. 02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies.

By Ruihang Li, Mengde Xu, Shuyang Gu, Leigang Qu, Fuli Feng, Han Hu, Wenjie Wang
arXiv Machine Learning
Jun 2

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606. 02521v1 Announce Type: new Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization.

By Zhou Jiang, Yandong Wen, Zhen Liu
arXiv Computer Vision
Oct 28

Visual Autoregressive Generation with Next Token Imagination

The paper introduces Next Token Imagination (NTI), a technique for visual autoregressive generation that allows a model to predict beyond the ground-truth next token while staying within a defined visual distribution. By enabling the model to explore plausible but unseen visual realizations, NTI addresses the limitation of training only on observed data. Experiments on ImageNet demonstrate that NTI consistently improves image fidelity, approaching the performance of state‑of‑the‑art diffusion and autoregressive models with a smaller model and lower training cost.

By Ruihang Jiang, Zongyao Zhao, Zhaolin Wang, Yuanwei Liu, Xiatian Zhu
Hugging Face Trending Papers
Oct 22

DRILL: Training World Models to Improve Policies, Not Predict Pixels

The paper introduces DRILL, a bilevel training framework that optimizes world models for policy improvement rather than pixel accuracy. DRILL-VWI reweights prediction errors using policy visitation and model confidence, while DRILL-IMG employs meta‑gradients to directly maximize downstream policy returns. Experiments on manipulation simulators show DRILL-IMG boosts success rates by 13.1 points over WMPO and 7.9 points over RLVR-World, demonstrating that world models can be more effectively trained using policy gradients.

arXiv Computer Vision
Oct 23

RECAP: Looking Once Is Not Enough for Vision-Language Reasoning

The paper introduces RECAP, a training method that enhances vision‑language models by incorporating temporally localized reactivation of image evidence—called visual revisit—into the reinforcement learning process. Visual revisit peaks are shown to predict correctness and causally improve performance; masking them hurts accuracy more than masking non‑peaks, while adding revisit peaks to failed reasoning partially restores correct answers. RECAP applies revisit‑conditioned advantage estimation, dynamic discounting, and gated value prediction to propagate final rewards through visually important reasoning steps, achieving significant gains on HallusionBench and other benchmarks without extra rewards or inference‑time changes.

By Zhaolu Kang, Tailong Luo, Chenxin Li, Yingjie He, Zhenyu Yu, Fengyu Zhou, Jiachen Qian, Lei Wei, Shuang Chen, Jiachen Li, Eric Hanchen Jiang, Rongchao Zhang, Zhengtao Yao, Hoi Leong Lee, Guansu Wang, Kaiyue Zhou