Lance: Unified Multimodal Modeling by Multi-Task Synergy
Authors: name: Fengyi Fu, name: Mengqi Huang, name: Shaojin Wu, name: Yunsheng Jiang, name: Yufei Huo, name: Hao Li, name: Yinghang Song, name: Fei Ding, name: Jianzhu Guo, name: Qian He, name: Zheren Fu, name: Zhendong Mao, name: Yongdong Zhang
Organization: bytedance-research

We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text-image-dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collaborative multi-task training. It is grounded in two core principles: unified context modeling and decoupled capability pathways. Specifically, Lance is trained from scratch and employs a dual-stream mixture-of-experts architecture on shared interleaved multimodal sequences, enabling joint context learning while decoupling the pathways for understanding and generation. We further introduce modality-aware rotary positional encoding to mitigate interference among heterogeneous visual tokens and boost cross-task alignment. During training, Lance adopts a staged multi-task training paradigm with capability-oriented objectives and adaptive data scheduling to strengthen both semantic comprehension and visual generation performance. Experimental results demonstrate that Lance substantially outperforms existing open-source unified models in image and video generation, while retaining strong multimodal understanding capabilities. The homepage is available at https://lance-project.github.io.