Scaling Multi-Reference Image Generation with Dynamic Reward Optimization

While personalized image generation has achieved remark-able progress, multi-reference image generation (MRIG) remains a chal-lenging task. Most existing benchmarks fail to adequately evaluate com-plex MRIG scenarios, hindering further progress in this area. To bet-ter assess model performance on complex MRIG tasks, we introduceOmniRef-Bench, a benchmark that covers complex combinations of ref-erence image types and a large number of reference images. Evaluationson OmniRef-Bench show that mainstream open-source models strugglein complex MRIG scenarios, and their performance deteriorates signifi-cantly as the number of mixed-type reference images increases. To ad-dress this issue, we propose DyRef, a two-stage training framework.In the first stage, supervised fine-tuning equips the model with the ba-sic capability to handle complex MRIG tasks. In the second stage, weintroduce Difficulty-aware Advantage Reweighting (DAR) and Discrim-inative Reward Scaling (DRS). DAR dynamically adjusts the optimiza-tion objective to improve performance when handling a large numberof mixed-type reference images. DRS enlarges intra-group reward differ-ences for more effective policy optimization. Experiments demonstratethat DyRef significantly improves the performance of open-source modelson OmniRef-Bench and single-image editing benchmarks, demonstratingthe effectiveness and generalization capability of our approach. Our codeis available at https://github.com/Weistrass/DyRef.