UniEvo-VL: Multimodal Models Improve Image Generation via Self-Feedback
StanfordAILab · x · 2026-10-05
New work UniEvo-VL (shared via Stanford AI Lab / Jure Leskovec) asks: when generation and understanding live in one multimodal model, can the model's feedback on its own outputs become a training signal—turning evaluation ability into generation gains—via on-policy self-distillation.
Built on Qwen-image-2512:
- GenEval: 0.747 → 0.808
- GenEval2 Soft-TIFA: 32.97 → 35.53
Gains show the promise of self-generated feedback, though its boundaries matter too.
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