Fang Wu

UniEvo-VL introduces an on-policy self-distillation (OPSD) recipe in which one multimodal model acts as both a student seeing only the vanilla prompt and a teacher conditioned on a critique-derived revised prompt, minimizing per-state divergence between their denoising distributions along the student's own trajectories; on Qwen-Image-2512 it raises GenEval from 0.747 to 0.808 and GenEval2 Soft-TIFA from 32.97 to 35.53, while stronger external critics such as GPT5.6-Luna indicate a higher self-evolving ceiling and text-rendering gains remain uneven.

UniEvo-VL introduces an on-policy self-distillation (OPSD) recipe in which one multimodal model acts as both a student seeing only the vanilla prompt and a teacher conditioned on a critique-derived revised prompt, minimizing per-state divergence between their denoising distributions along the student's own trajectories; on Qwen-Image-2512 it raises GenEval from 0.747 to 0.808 and GenEval2 Soft-TIFA from 32.97 to 35.53, while stronger external critics such as GPT5.6-Luna indicate a higher self-evolving ceiling and text-rendering gains remain uneven.