Kwai Keye-VL Technical Report
Authors: name: Kwai Keye Team, name: Biao Yang, name: Bin Wen, name: Changyi Liu, name: Chenglong Chu, name: Chengru Song, name: Chongling Rao, name: Chuan Yi, name: Da Li, name: Dunju Zang, name: Fan Yang, name: Guorui Zhou, name: Hao Peng, name: Haojie Ding, name: Jiaming Huang, name: Jiangxia Cao, name: Jiankang Chen, name: Jingyun Hua, name: Jin Ouyang, name: Kaibing Chen, name: Kaiyu Jiang, name: Kaiyu Tang, name: Kun Gai, name: Shengnan Zhang, name: Siyang Mao, name: Sui Huang, name: Tianke Zhang, name: Tingting Gao, name: Wei Chen, name: Wei Yuan, name: Xiangyu Wu, name: Xiao Hu, name: Xingyu Lu, name: Yang Zhou, name: Yi-Fan Zhang, name: Yiping Yang, name: Yulong Chen, name: Zhenhua Wu, name: Zhenyu Li, name: Zhixin Ling, name: Ziming Li, name: Dehua Ma, name: Di Xu, name: Haixuan Gao, name: Hang Li, name: Jiawei Guo, name: Jing Wang, name: Lejian Ren, name: Muhao Wei, name: Qianqian Wang, name: Qigen Hu, name: Shiyao Wang, name: Tao Yu, name: Xinchen Luo, name: Yan Li, name: Yiming Liang, name: Yuhang Hu, name: Zeyi Lu, name: Zhuoran Yang, name: Zixing Zhang

While Multimodal Large Language Models (MLLMs) demonstrate remarkable
capabilities on static images, they often fall short in comprehending dynamic,
information-dense short-form videos, a dominant medium in today's digital
landscape. To bridge this gap, we introduce Kwai Keye-VL, an
8-billion-parameter multimodal foundation model engineered for leading-edge
performance in short-video understanding while maintaining robust
general-purpose vision-language abilities. The development of Keye-VL rests on
two core pillars: a massive, high-quality dataset exceeding 600 billion tokens
with a strong emphasis on video, and an innovative training recipe. This recipe
features a four-stage pre-training process for solid vision-language alignment,
followed by a meticulous two-phase post-training process. The first
post-training stage enhances foundational capabilities like instruction
following, while the second phase focuses on stimulating advanced reasoning. In
this second phase, a key innovation is our five-mode ``cold-start'' data
mixture, which includes ``thinking'', ``non-thinking'', ``auto-think'', ``think
with image'', and high-quality video data. This mixture teaches the model to
decide when and how to reason. Subsequent reinforcement learning (RL) and
alignment steps further enhance these reasoning capabilities and correct
abnormal model behaviors, such as repetitive outputs. To validate our approach,
we conduct extensive evaluations, showing that Keye-VL achieves
state-of-the-art results on public video benchmarks and remains highly
competitive on general image-based tasks (Figure 1). Furthermore, we develop
and release the KC-MMBench, a new benchmark tailored for real-world
short-video scenarios, where Keye-VL shows a significant advantage.