Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision
Authors: name: Zhixiang Wei, name: Yi Li, name: Zhehan Kan, name: Xinghua Jiang, name: Zuwei Long, name: Shifeng Liu, name: Hongze Shen, name: Wei Liu, name: Xiaoyu Tan, name: Haojia Lin, name: Yubo Zhu, name: Qianyu Li, name: Di Yin, name: Haoyu Cao, name: Weibo Gu, name: Xin Li, name: Yinsong Liu, name: Deqiang Jiang, name: Xing Sun, name: Yunsheng Wu, name: Mingkong Tang, name: Shuangyin Liu, name: Lexiang Tang, name: Haodong Lin, name: Junru Lu, name: Jiarui Qin, name: Lingfeng Qiao, name: Ruizhi Qiao, name: Bo Ke, name: Jianfeng He, name: Ke Li, name: Yangning Li, name: Yunhang Shen, name: Mengdan Zhang, name: Peixian Chen, name: Kun Yin, name: Bing Liu, name: Yunfei Wu, name: Huang Chen, name: Zhongpeng Cai, name: Xiaotian Li
Organization: Tencent

Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, leading to coarse-grained multimodal comprehension. We attribute this deficiency to a suboptimal training paradigm inherent in prevailing VLMs, which exhibits a text-dominant optimization bias by conceptualizing visual signals merely as passive conditional inputs rather than supervisory targets. To mitigate this, we introduce Youtu-VL, a framework leveraging the Vision-Language Unified Autoregressive Supervision (VLUAS) paradigm, which fundamentally shifts the optimization objective from ``vision-as-input'' to ``vision-as-target.'' By integrating visual tokens directly into the prediction stream, Youtu-VL applies unified autoregressive supervision to both visual details and linguistic content. Furthermore, we extend this paradigm to encompass vision-centric tasks, enabling a standard VLM to perform vision-centric tasks without task-specific additions. Extensive empirical evaluations demonstrate that Youtu-VL achieves competitive performance on both general multimodal tasks and vision-centric tasks, establishing a robust foundation for the development of comprehensive generalist visual agents.