title: VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement
    Fine-Tuning
summary: Recent advancements in reinforcement learning have significantly advanced the
  reasoning capabilities of multimodal large language models (MLLMs). While
  approaches such as Group Relative Policy Optimization (GRPO) and rule-based
  reward mechanisms demonstrate promise in text and image domains, their
  application to video understanding remains limited. This paper presents a
  systematic exploration of Reinforcement Fine-Tuning (RFT) with GRPO for video
  MLLMs, aiming to enhance spatio-temporal perception while maintaining general
  capabilities. Our experiments reveal that RFT is highly data-efficient for
  task-specific improvements. Through multi-task RFT on spatio-temporal
  perception objectives with limited samples, we develop VideoChat-R1, a powerful
  video MLLM that achieves state-of-the-art performance on spatio-temporal
  perception tasks without sacrificing chat ability, while exhibiting emerging
  spatio-temporal reasoning abilities. Compared to Qwen2.5-VL-7B, VideoChat-R1
  boosts performance several-fold in tasks like temporal grounding (+31.8) and
  object tracking (+31.2). Additionally, it significantly improves on general QA
  benchmarks such as VideoMME (+0.9), MVBench (+1.0), and Perception Test (+0.9).
  Our findings underscore the potential of RFT for specialized task enhancement
  of Video MLLMs. We hope our work offers valuable insights for future RL
  research in video MLLMs.
paper_authors._id: 67f783fc2eec6ce5c8d18be3
paper_authors.name: Xinhao Li
paper_authors.status: admin_assigned
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paper_authors.fullname: XINHAO LI
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paper_authors.user: xinhaoli
paper_authors._id: 67f783fc2eec6ce5c8d18be4
paper_authors.name: Ziang Yan
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paper_authors.fullname: ziang yan
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paper_authors.user: Aurorana
paper_authors._id: 67f783fc2eec6ce5c8d18be5
paper_authors.name: Desen Meng
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paper_authors.fullname: Desen Meng
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paper_authors.user: desenmeng
paper_authors._id: 67f783fc2eec6ce5c8d18be6
paper_authors.name: Lu Dong
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/84511d4cd2a6bdc229dd2b1057d4b2ab.svg
paper_authors.fullname: Lu Dong
paper_authors.type: user
paper_authors.user: donglu
paper_authors._id: 67f783fc2eec6ce5c8d18be7
paper_authors.name: Xiangyu Zeng
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/ceff1231078115cae8f3f4f87d026963.svg
paper_authors.fullname: Xiangyu Zeng
paper_authors.type: user
paper_authors.user: Lanxingxuan
paper_authors._id: 67f783fc2eec6ce5c8d18be8
paper_authors.name: Yinan He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-10T08:53:32.159Z
paper_authors._id: 65b9d9961fe588f824fde191
paper_authors.avatarUrl: /avatars/a9245958cc998a4b4b870bf2490fdaee.svg
paper_authors.fullname: Yinan He
paper_authors.type: user
paper_authors.user: yinanhe
paper_authors._id: 67f783fc2eec6ce5c8d18be9
paper_authors.name: Yali Wang
paper_authors._id: 67f783fc2eec6ce5c8d18bea
paper_authors.name: Yu Qiao
paper_authors._id: 67f783fc2eec6ce5c8d18beb
paper_authors.name: Yi Wang
paper_authors._id: 67f783fc2eec6ce5c8d18bec
paper_authors.name: Limin Wang
paper_authors.status: admin_assigned
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paper_title: VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement
    Fine-Tuning
paper_summary: Recent advancements in reinforcement learning have significantly advanced the
  reasoning capabilities of multimodal large language models (MLLMs). While
  approaches such as Group Relative Policy Optimization (GRPO) and rule-based
  reward mechanisms demonstrate promise in text and image domains, their
  application to video understanding remains limited. This paper presents a
  systematic exploration of Reinforcement Fine-Tuning (RFT) with GRPO for video
  MLLMs, aiming to enhance spatio-temporal perception while maintaining general
  capabilities. Our experiments reveal that RFT is highly data-efficient for
  task-specific improvements. Through multi-task RFT on spatio-temporal
  perception objectives with limited samples, we develop VideoChat-R1, a powerful
  video MLLM that achieves state-of-the-art performance on spatio-temporal
  perception tasks without sacrificing chat ability, while exhibiting emerging
  spatio-temporal reasoning abilities. Compared to Qwen2.5-VL-7B, VideoChat-R1
  boosts performance several-fold in tasks like temporal grounding (+31.8) and
  object tracking (+31.2). Additionally, it significantly improves on general QA
  benchmarks such as VideoMME (+0.9), MVBench (+1.0), and Perception Test (+0.9).
  Our findings underscore the potential of RFT for specialized task enhancement
  of Video MLLMs. We hope our work offers valuable insights for future RL
  research in video MLLMs.
paper_ai_summary: Reinforcement Fine-Tuning with Group Relative Policy Optimization enhances video multimodal large language models, improving spatio-temporal perception without compromising chat abilities.
paper_ai_keywords: Group Relative Policy Optimization Reinforcement Fine-Tuning video multimodal large language models spatio-temporal perception temporal grounding object tracking VideoMME MVBench Perception Test