Kwai Keye-VL-2.0 Technical Report
Authors: name: Kwai Keye Team, name: Bin Wen, name: Changyi Liu, name: Chengru Song, name: Chongling Rao, name: Guowang Zhang, name: Han Li, name: Haonan Fan, name: Hengrui Ju, name: Jiankang Chen, name: Jiapeng Chen, name: Jiawei Yuan, name: Kaixuan Yang, name: Kaiyu Jiang, name: Kun Gai, name: Lingzhi Zhou, name: Na Nie, name: Sen Na, name: Tianke Zhang, name: Tingting Gao, name: Xuanyu Zheng, name: Yulong Chen, name: Fan Yang, name: Haixuan Gao, name: Lele Yang, name: Mingqiao Liu, name: Muxi Diao, name: Qi Zhang, name: Qile Su, name: Wei Chen, name: Wentao Hong, name: Xingyu Lu, name: Yancheng Long, name: Yankai Yang, name: Yingxin Li, name: Yiyang Fan, name: Yu Xia, name: Yuzhe Chen, name: Ziliang Lai, name: Chuan Yi, name: Haonan Jia, name: Tianming Liang, name: Weixin Xu, name: Xiaoxiao Ma, name: Yang Tian, name: Yufei Han, name: Feng Han, name: Hang Li, name: Jing Wang, name: Jinghui Jia, name: Junmin Chen, name: Junyu Shi, name: Ruilin Zhang
Organization: Kwai Keye

We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding and agentic intelligence. To address the challenges of ultra-long contexts, information redundancy, and prohibitive computational costs inherent in hour-level videos, Keye-VL-2.0 is the first to adapt DeepSeek Sparse Attention (DSA) to GQA-based multimodal architectures, enabling lossless 256K context processing while capturing critical frames and long-range temporal dependencies. This architecture is underpinned by a highly optimized training and inference infrastructure, including scalable video I/O, heterogeneous ViT-LM parallelism, and custom DSA kernels that significantly maximize throughput and minimize computational overhead. Furthermore, to overcome the algorithmic dilemma of catastrophic forgetting during multi-task alignment, we introduce Cross-Modal Multi-Teacher On-Policy Distillation (MOPD) paired with Context-RL and Video-RL. By distilling dense token-level teacher feedback from on-policy rollouts back into the MoE backbone, which activates only 3B parameters, Keye-VL-2.0 natively empowers advanced agent collaboration across Code, Tool, and Search scenarios with multimodal self-correction. Extensive evaluations across video understanding, temporal grounding, reasoning, STEM, and agent benchmarks demonstrate that Keye-VL-2.0-30B-A3B achieves state-of-the-art performance among models of similar scale, particularly excelling in fine-grained temporal localization on TimeLens and long-video comprehension on Video-MME-v2 and LongVideoBench. We release our model checkpoints to accelerate community progress toward scalable and robust multimodal agentic applications.