arXiv AI
UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement
Read the original on arXiv AI →UniEvo‑VL is a self‑evolving framework that lets multimodal models improve themselves by using their own critiques as privileged information. The method trains a single model to act as both teacher and student, minimizing divergence between their diffusion distributions over sampling trajectories. Experiments on Qwen‑image‑2512 show significant gains in image generation metrics, and stronger external critics further raise the improvement ceiling, though results vary across tasks.
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