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UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement
An image model critiques its own pictures, then trains itself to get them right the first time.
0.808
GenEval score (0–1)
GenEval score after self-training
No external teacher or reward model needed for the gain
How it works
Critiques help only once
A unified model can spot flaws in its own images, but fixing them needs an extra reflection pass every time.
Same model, two roles
The teacher copy sees a critique-revised prompt; the student sees the original. The student matches the teacher's denoising steps.
Better first drafts
Direct generation improves on GenEval, GenEval2 and OCR. Reflection still adds gains on top.
From the paper