Personalized Reward Modeling for Text-to-Image Generation

Recent text-to-image (T2I) models generate semantically co-herent images from textual prompts, yet evaluating how well they alignwith individual user preferences remains an open challenge. Conventionalevaluation methods, general reward functions or similarity-based metrics,fail to capture the diversity and complexity of personal visual tastes. Wepresent PIGReward, a personalized reward modeling framework that dy-namically generates user-conditioned evaluation dimensions and assessesimages through CoT reasoning. To address the scarcity of user data, PI-GReward adopts a self-bootstrapping strategy that reasons over limitedreference data to construct rich user contexts, enabling personalizationwithout user-specix001Cc training. Beyond evaluation, PIGReward providespersonalized feedback that drives user-specix001Cc prompt optimization, im-proving alignment between generated images and individual intent. Ex-tensive experiments demonstrate that PIGReward outperforms existingmethods in both accuracy and interpretability, establishing a scalable,reasoning-based foundation for personalized T2I evaluation and opti-mization. Project page: https://jeongeunnn-e/PIGReward/