Qwen-Image-2.0-RL Technical Report
Authors: name: Yixian Xu, name: Kaiyuan Gao, name: Yuxiang Chen, name: Yilei Chen, name: Zecheng Tang, name: Zihao Liu, name: Zikai Zhou, name: Deqing Li, name: Hao Meng, name: Kuan Cao, name: Jiahao Li, name: Jie Zhang, name: Liang Peng, name: Lihan Jiang, name: Ningyuan Tang, name: Shengming Yin, name: Tianhe Wu, name: Xiaoyue Chen, name: Yan Shu, name: Yanran Zhang, name: Yi Wang, name: Yu Wu, name: Yujia Wu, name: Zekai Zhang, name: Zhendong Wang, name: Xiao Xu, name: Kun Yan, name: Chenfei Wu
Organization: Qwen

We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual quality and instruction-following capability of the Qwen-Image-2.0 diffusion model. To provide reliable reward signals, we construct task-specific composite reward models by fine-tuning vision-language models with a pointwise scoring paradigm and chain-of-thought reasoning. For text-to-image generation, the reward models cover alignment, aesthetics, and portrait fidelity dimensions. For image editing tasks, the reward system addresses instruction-following accuracy and face identity preservation. Building on this reward system, we develop a scalable GRPO-based RL training framework, incorporating a hybrid classifier-free guidance (CFG) strategy to preserve pre-trained knowledge, prompt curation via intra-group reward range filtering, and per-category reward weight calibration. To merge the task-specialized RL policies for T2I and editing, we propose on-policy distillation as the final training stage, which consolidates multiple teachers into a single student model through trajectory-level velocity matching. Extensive evaluation shows that Qwen-Image-2.0-RL achieves 57.84 overall score on Qwen-Image-Bench (+2.61 over the base model), Elo ratings of 1193 in text-to-image arena (+78) and 1349 in image edit arena (+93), demonstrating consistent gains in aesthetic quality, prompt adherence, and editing accuracy.