AI画画也能用RL调教，效果肉眼可见
AI画图模型通常靠大量数据训练，但生成结果常偏离用户指令。这篇论文把强化学习（RL）用在了图像生成上：先训练多个奖励模型（分别评估美观度、指令匹配、人像保真等），再用这些奖励信号去优化生成模型，让模型学会自己调整输出。最终模型在图像生成和编辑任务上均显著提升，用户偏好评分提高78-93分。这不是你明天能直接用的工具，但它展示了AI画图从“猜你想要”到“听你指挥”的关键一步。
📄 原文摘要(英文)
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.