Kwai Keye-VL 1.5 Technical Report
Authors: name: Biao Yang, name: Bin Wen, name: Boyang Ding, 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: Guowang Zhang, name: Han Shen, name: Hao Peng, name: Haojie Ding, name: Hao Wang, name: Hengrui Ju, name: Jiaming Huang, name: Jiangxia Cao, name: Jiankang Chen, name: Jingyun Hua, name: Kaibing Chen, name: Kaiyu Jiang, name: Kaiyu Tang, name: Kun Gai, name: Muhao Wei, name: Qiang Wang, name: Ruitao Wang, name: Sen Na, 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: Yi-Fan Zhang, name: Yiping Yang, name: Yulong Chen, name: Zeyi Lu, name: Zhenhua Wu, name: Zhixin Ling, name: Zhuoran Yang, name: Ziming Li, name: Di Xu, name: Haixuan Gao, name: Hang Li, name: Jing Wang, name: Lejian Ren, name: Qigen Hu, name: Qianqian Wang, name: Shiyao Wang, name: Xinchen Luo, name: Yan Li, name: Yuhang Hu, name: Zixing Zhang

In recent years, the development of Large Language Models (LLMs) has
significantly advanced, extending their capabilities to multimodal tasks
through Multimodal Large Language Models (MLLMs). However, video understanding
remains a challenging area due to the dynamic and information-dense nature of
videos. Existing models struggle with the trade-off between spatial resolution
and temporal coverage when processing video content. We present Keye-VL-1.5,
which addresses fundamental challenges in video comprehension through three key
innovations. First, we introduce a novel Slow-Fast video encoding strategy that
dynamically allocates computational resources based on inter-frame similarity,
processing key frames with significant visual changes at higher resolution
(Slow pathway) while handling relatively static frames with increased temporal
coverage at lower resolution (Fast pathway). Second, we implement a progressive
four-stage pre-training methodology that systematically extends the model's
context length from 8K to 128K tokens, enabling processing of longer videos and
more complex visual content. Third, we develop a comprehensive post-training
pipeline focusing on reasoning enhancement and human preference alignment,
incorporating a 5-step chain-of-thought data construction process, iterative
GSPO-based reinforcement learning with progressive prompt hinting for difficult
cases, and alignment training. Through extensive evaluation on public
benchmarks and rigorous internal human assessment, Keye-VL-1.5 demonstrates
significant improvements over existing models, particularly excelling in video
understanding tasks while maintaining competitive performance on general
multimodal benchmarks.