这篇论文提出STAR方法，通过空间和时间自适应分配奖励，让RL后训练更精准地优化文本到图像生成，效果在GenEval等基准上显著提升。
STAR方法针对文本到图像生成的RL后训练中的奖励粒度不匹配问题，提出时空自适应奖励分配。它利用生成模型内的文本-图像注意力，在去噪步骤和生成过程中动态构建空间分配图，将组相对优势分配给更相关的潜在区域。以Stable Diffusion 3.5 Medium为基础模型，在GenEval、OCR文本渲染和PickScore三项任务上分别达到0.9759、0.9757和23.60的分数。
STAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory. However, text-to-image generation naturally has temporal and spatial structure: different denoising steps are responsible for different generation stages, and the content that truly determines text alignment often appears only in part of the image. This granularity mismatch makes it difficult for policy updates to focus on the generative components that actually affect the reward. To address this issue, we propose \textbf{SpatioTemporal Adaptive Reward (STAR) Allocation} for RL post-training of text-to-image diffusion and flow models. STAR uses text-image attention inside the generative model and starts from the core content that the user truly cares about in the prompt. It constructs spatial allocation maps that dynamically vary across denoising steps and rollouts, and allocates the same group-relative advantage to more relevant latent regions with almost no additional computational overhead. STAR then applies stronger policy updates to these regions through a spatially resolved policy objective. We use Stable Diffusion 3.5 Medium as the base model and evaluate on three tasks: GenEval, OCR text rendering, and PickScore. Experimental results show that STAR improves compositional semantic alignment, text rendering, and preference optimization without changing the external reward source, achieving $\mathbf{0.9759}$, $\mathbf{0.9757}$, and $\mathbf{23.60}$ on GenEval, OCR, and PickScore, respectively.