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UniEvo-VL：面向多模态模型自我改进的在线自蒸馏训练方案
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UniEvo-VL 提出一种无需外部教师模型的多模态自进化训练方案，让同一模型分别以学生和教师身份、基于不同上下文进行在线自蒸馏，在自身采样轨迹上最小化去噪扩散分布差异。
正文
Authors:Fang Wu, Da Xing, Yanjie Huang, Junxi Wang, Ji Wang, Hejia Geng, Guancheng Wan, Bowen Zuo, Xiaomin Li, Shixiang Tang, Xinyu Xiang, Zehong Wang, Shiyi Du, Peng Xia, Shuangjia Zheng, Yining Hong, Li Erran Li, Jure Leskovec, Yejin Choi
Abstract:Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback during test-time compute. Instead of relying on a separate, often larger, teacher, we leverage their self-critiques as privileged information and ask a single multimodal model to act as both teacher and student with different contexts. The student only sees the vanilla question, while the teacher conditions on the privileged critique. Then training minimizes the per-state divergence between their denoising diffusion distributions over the student's own sampling trajectories. Experiments demonstrate that UniEvo-VL improves the image generation capabilities of multimodal models, while maintaining their sensitivity to additional reflection information. Specifically, we build on top of the open-source Qwen-image-2512 and observe a significant performance gain from 0.747 to 0.808 on GenEval and from 32.97 to 35.53 on GenEval2 Soft-TIFA. Moreover, attempts with more powerful external critics (e.g., GPT5.6-Luna) show that multimodal models with strong judge capabilities can anticipate a higher self-evolving ceiling. Last but not least, mixed text-rendering outcomes show that our self-improvements may not be uniform across different tasks. Our study aims to shed light on the current hot recursive self-improvement research line to enhance the user experience when using multimodal models without external supervision or guidance.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2609.38721 [cs.AI] |
| (or arXiv:2609.38721v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38721 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fang Wu [view email]
[v1]
Wed, 30 Sep 2026 00:54:09 UTC (42,272 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org