Video-R1: Reinforcing Video Reasoning in MLLMs
Authors: name: Kaituo Feng, name: Kaixiong Gong, name: Bohao Li, name: Zonghao Guo, name: Yibing Wang, name: Tianshuo Peng, name: Benyou Wang, name: Xiangyu Yue

Inspired by DeepSeek-R1's success in eliciting reasoning abilities through
rule-based reinforcement learning (RL), we introduce Video-R1 as the first
attempt to systematically explore the R1 paradigm for eliciting video reasoning
within multimodal large language models (MLLMs). However, directly applying RL
training with the GRPO algorithm to video reasoning presents two primary
challenges: (i) a lack of temporal modeling for video reasoning, and (ii) the
scarcity of high-quality video-reasoning data. To address these issues, we
first propose the T-GRPO algorithm, which encourages models to utilize temporal
information in videos for reasoning. Additionally, instead of relying solely on
video data, we incorporate high-quality image-reasoning data into the training
process. We have constructed two datasets: Video-R1-COT-165k for SFT cold start
and Video-R1-260k for RL training, both comprising image and video data.
Experimental results demonstrate that Video-R1 achieves significant
improvements on video reasoning benchmarks such as VideoMMMU and VSI-Bench, as
well as on general video benchmarks including MVBench and TempCompass, etc.
Notably, Video-R1-7B attains a 35.8% accuracy on video spatial reasoning
benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o. All
codes, models, data are released.