Lance：通过多任务协同实现统一多模态建模

摘要

我们提出了Lance——一个轻量级原生统一模型，支持图像和视频的多模态理解、生成与编辑。不同于依赖模型规模扩展或文本-图像主导的设计，Lance探索了一种通过协同多任务训练实现统一多模态建模的实用范式。该模型基于两大核心原则：统一上下文建模与解耦能力路径。具体而言，Lance从零开始训练，在共享的交错多模态序列上采用双流混合专家架构，实现了联合上下文学习的同时，将理解与生成的路径解耦。我们还引入了模态感知的旋转位置编码，以减轻异构视觉标记间的干扰并提升跨任务对齐能力。训练过程中，Lance采用分阶段多任务训练范式，结合面向能力的优化目标与自适应数据调度，同时强化语义理解与视觉生成性能。实验结果表明，Lance在图像和视频生成任务上显著优于现有的开源统一模型，同时保持了强大的多模态理解能力。项目主页：https://lance-project.github.io。

English

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.