SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models

Recently, remarkable progress has been made in Unix001Ced Mul-timodal Models (UMMs), which integrate vision-language generation andunderstanding capabilities within a single framework. However, a model'sstrong visual understanding often fails to transfer to visual generation:it may correctly judge prompt-image alignment while failing to gener-ate a faithful image from the same prompt. This raises a compellingquestion: Can a model improve itself by using its understanding moduleto reward its generation module? We introduce SRUM, a self-rewardingpost-training framework directly applicable to existing UMMs of vari-ous designs. SRUM creates a feedback loop where the model's own un-derstanding module acts as an internal x0010evaluatorx0011, providing correctivesignals to improve generation without additional human-labeled data orexternal reward models. To provide comprehensive feedback, SRUM usesa global-local dual reward system: a global reward ensures overall vi-sual semantics and layout, while a local reward rex001Cnes x001Cne-grained,object-level x001Cdelity. SRUM shows strong generalization, boosting perfor-mance on T2I-CompBench from 82.18 to 88.37 and on T2I-ReasonBenchfrom 43.82 to 46.75. Overall, our work establishes a powerful paradigmfor enabling a UMM's understanding module to guide and enhance itsown generation via self-rewarding.