UniEvo-VL improves image generation without a separate teacher
UniEvo-VL lets one multimodal model improve its image generation by using its own critiques, instead of relying on a separate, often larger teacher. The model’s student version sees the original prompt, while its teacher version also sees a critique, then training aligns their outputs along the student’s own sampling paths. On open-source Qwen-image-2512, this raised GenEval from 0.747 to 0.808 and GenEval2 Soft-TIFA from 32.97 to 35.53, though gains were not uniform across tasks.