Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen, Changyi Liu, Chenglong Chu, Chengru Song, Chongling Rao, Chuan Yi, Da Li, Dunju Zang, Fan Yang, Guorui Zhou, Hao Peng, Haojie Ding, Jiaming Huang, Jiangxia Cao, Jiankang Chen, Jingyun Hua, Jin Ouyang, Kaibing Chen, Kaiyu Jiang, Kaiyu Tang
2025-07-03
Summary
This paper talks about Kwai Keye-VL, an 8-billion-parameter multimodal model designed especially to understand short videos and general vision-language tasks. It uses a large dataset and innovative training process to improve how AI comprehends and reasons about videos.
What's the problem?
The problem is that many AI models struggle to understand short, fast-changing videos well, even though these videos are very common on social media platforms. Existing models often perform better on static images but have difficulty with the complex information in videos.
What's the solution?
The researchers built Kwai Keye-VL with a vision transformer and language decoder specially trained with a huge dataset that includes a mixture of five different data modes. They use a two-phase training process that first builds strong basic skills and then teaches advanced reasoning. Reinforcement learning helps the model improve its reasoning and avoid repetitive mistakes.
Why it matters?
This matters because understanding short videos better helps AI provide more useful and accurate insights for platforms relying on video content, such as entertainment, e-commerce, and social media, making interactions with multimedia smarter and more natural.
Abstract
Kwai Keye-VL, an 8-billion-parameter multimodal model, excels in short-video understanding and general vision-language tasks through a comprehensive pre-training and post-training process, including a five-mode data mixture and reinforcement learning.