ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
Qwen-Image-2.0-RL Technical Report
A diffusion-model post-training pipeline combines task-specific rewards, reinforcement learning and on-policy distillation.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Compare improvements in instruction following with the reward design and evaluation scope.
Citation
@misc{xu2026qwen,
title = {Qwen-Image-2.0-RL Technical Report},
author = {Yixian Xu and Kaiyuan Gao and Yuxiang Chen and Yilei Chen and Zecheng Tang and Zihao Liu and Zikai Zhou and Deqing Li and Hao Meng and Kuan Cao and Jiahao Li and Jie Zhang and Liang Peng and Lihan Jiang and Ningyuan Tang and Shengming Yin and Tianhe Wu and Xiaoyue Chen and Yan Shu and Yanran Zhang and Yi Wang and Yu Wu and Yujia Wu and Zekai Zhang and Zhendong Wang and Xiao Xu and Kun Yan and Chenfei Wu},
year = {2026},
eprint = {2606.27608},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.27608}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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