EasyVideoR1: Easier RL for Video Understanding
Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu, Dingyu Yao, Zheng Lin, Peng Fu, Nan Duan, Jiaqi Wang
2026-04-21
Summary
This paper introduces EasyVideoR1, a new framework for improving how well large AI models understand videos using a technique called reinforcement learning from verifiable rewards. It's designed to make training these models on video tasks much more efficient and effective.
What's the problem?
Training AI models to understand videos is really hard. Videos are complex, with lots of different types of tasks you might want the AI to do. Processing all that visual information takes a lot of computing power, and it's difficult to make sure the results are consistent and reliable. Existing tools for reinforcement learning work well with text and images, but they aren't optimized for the specific challenges of video data.
What's the solution?
The researchers created EasyVideoR1, which tackles these problems in several ways. First, it pre-processes videos and stores the data efficiently to speed up training. Second, it includes a system for giving the AI clear rewards based on how well it performs on 11 different video and image tasks. Third, it combines pre-existing good examples with the AI learning through trial and error. Fourth, it allows the AI to learn from both images and videos at the same time, helping each modality improve. Finally, it provides a way to automatically test the AI's performance on many different video understanding challenges.
Why it matters?
This work is important because it makes it much easier to build AI systems that can truly understand what's happening in videos. This has huge implications for things like self-driving cars, robotics, video analysis, and many other applications where AI needs to interpret the visual world.
Abstract
Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for training large vision-language models on video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant video decoding and yields a 1.47 times throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-benchmark evaluation framework covering 22 mainstream video understanding benchmarks, with reproduced accuracy closely aligned with officially reported scores.