title: Video-R1: Reinforcing Video Reasoning in MLLMs
summary: 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.
paper_authors._id: 67e6090248742d6df75853ae
paper_authors.name: Kaituo Feng
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paper_authors.name: Kaixiong Gong
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paper_authors.fullname: Kaixiong Gong
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paper_authors.name: Bohao Li
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paper_authors.fullname: Bohao Li
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paper_authors.user: BreakLee
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paper_authors.name: Zonghao Guo
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paper_authors.fullname: Zonghao Guo
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paper_authors.user: guozonghao96
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paper_authors.name: Yibing Wang
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paper_authors.name: Tianshuo Peng
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paper_authors.fullname: Tianshuo Peng
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paper_authors.user: Potentialts
paper_authors._id: 67e6090248742d6df75853b4
paper_authors.name: Benyou Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: Benyou
paper_authors._id: 67e6090248742d6df75853b5
paper_authors.name: Xiangyu Yue
paper_authors.status: admin_assigned
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paper_authors.fullname: Xiangyu Yue
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paper_title: Video-R1: Reinforcing Video Reasoning in MLLMs
paper_summary: 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.
paper_ai_summary: Video-R1, leveraging rule-based reinforcement learning and temporal information, enhances video reasoning in multimodal large language models using a combination of video and image data.
paper_ai_keywords: rule-based reinforcement learning RL GRPO algorithm T-GRPO algorithm temporal modeling multimodal large language models MLLMs SFT cold start video reasoning benchmarks VideoMMMU VSI-Bench MVBench TempCompass video spatial reasoning