Video-R1: Reinforcing Video Reasoning in MLLMs
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.
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Kaituo Feng
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2025-03-28T08:40:54.494Z
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Kaituo Feng
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Kaixiong Gong
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2025-03-28T08:41:00.671Z
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Kaixiong Gong
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Bohao Li
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2025-03-28T08:37:22.901Z
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Bohao Li
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BreakLee
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Zonghao Guo
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2025-03-28T08:41:07.167Z
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Zonghao Guo
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guozonghao96
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Yibing Wang
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Tianshuo Peng
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2025-03-28T08:41:29.644Z
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Tianshuo Peng
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Potentialts
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Benyou Wang
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2025-03-28T08:41:41.619Z
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Wang
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Benyou
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Xiangyu Yue
admin_assigned
2025-03-28T08:41:48.267Z
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Xiangyu Yue
user
xyyue
Video-R1: Reinforcing Video Reasoning in MLLMs
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.
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.
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