Scaling RL to Long Videos
Yukang Chen, Wei Huang, Baifeng Shi, Qinghao Hu, Hanrong Ye, Ligeng Zhu, Zhijian Liu, Pavlo Molchanov, Jan Kautz, Xiaojuan Qi, Sifei Liu, Hongxu Yin, Yao Lu, Song Han
2025-07-11
Summary
This paper talks about a new framework that helps AI models understand and reason about long videos better by using reinforcement learning techniques to improve how they process and analyze many video frames.
What's the problem?
The problem is that most AI models struggle to analyze long videos because handling many frames at once is very complex and requires a lot of computing power. This makes it hard for AI to keep track of long events and understand the full story.
What's the solution?
The researchers designed a system that uses reinforcement learning to train vision-language models to focus on important parts of long videos and connect information across many frames. This approach improves the model's ability to reason over extended video content effectively.
Why it matters?
This matters because videos are everywhere, and better AI understanding of long videos can improve applications like video search, security monitoring, and storytelling, making technology more useful and smarter.
Abstract
A framework scales vision-language models for long video reasoning using reinforcement learning, achieving strong performance on benchmarks and demonstrating consistent gains with increased video frames.