Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step generative ISR, while one-step generative ISR remains underexplored due to its limited stochasticity. In addition, RL methods such as Direct Preference Optimization (DPO) require the generation of positive and negative sample pairs offline, leading to a limited number of samples, while Group Relative Policy Optimization (GRPO) only calculates the likelihood of the entire image, ignoring local details that are crucial for ISR. In this paper, we propose Group Direct Preference Optimization (GDPO), a novel approach to integrate RL into one-step generative ISR model training. First, we introduce a noise-aware one-step diffusion model that can generate diverse ISR outputs. To prevent performance degradation caused by noise injection, we introduce an unequal-timestep strategy to decouple the timestep of noise addition from that of diffusion. We then present the GDPO strategy, which integrates the principle of GRPO into DPO, to calculate the group-relative advantage of each online generated sample for model optimization. Meanwhile, an attribute-aware reward function is designed to dynamically evaluate the score of each sample based on its statistics of smooth and texture areas. Experiments demonstrate the effectiveness of GDPO in enhancing the performance of one-step generative ISR models. Code: https://github.com/Joyies/GDPO.
RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；OPPO AI Center, OPPO Inc.（OPPO人工智能中心）
GlyphPrinter: Region-Grouped Direct Preference Optimization for Glyph-Accurate Visual Text Rendering
Institute of Big Data, College of Computer Science and Artificial Intelligence, Fudan University（大数据研究院，计算机科学与人工智能学院，复旦大学）；Generative AI Lab, College of Computing and Data Science, Nanyang Technological University（生成式人工智能实验室，计算与数据科学学院，南洋理工大学）
Model Agnostic Preference Optimization for Medical Image Segmentation
DGIST, School of Undergrad Studies（DGIST研究生院）；DGIST, Department of Robotics and Mechatronics Engineering（DGIST机器人与机电工程系）；DGIST, Division of Intelligent Robot（DGIST智能机器人系）
Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
University of Science and Technology of China（中国科学技术大学）；Xiaomi Corporation（小米公司）；State Key Laboratory of Communication Content Cognition, People’s Daily Online（人民日报社传播内容认知国家重点实验室）
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Shenzhen Loop Area Institute（深圳河套学院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）
