VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement
  Fine-Tuning
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.
67f783fc2eec6ce5c8d18be3
Xinhao Li
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2025-04-10T08:52:46.553Z
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XINHAO LI
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Ziang Yan
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2025-04-10T08:52:52.960Z
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ziang yan
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Aurorana
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Desen Meng
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2025-04-10T08:53:00.046Z
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Desen Meng
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Lu Dong
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2025-04-10T08:53:11.431Z
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Lu Dong
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donglu
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Xiangyu Zeng
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2025-04-10T08:53:25.534Z
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Xiangyu Zeng
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Lanxingxuan
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Yinan He
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2025-04-10T08:53:32.159Z
65b9d9961fe588f824fde191
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Yinan He
user
yinanhe
67f783fc2eec6ce5c8d18be9
Yali Wang
67f783fc2eec6ce5c8d18bea
Yu Qiao
67f783fc2eec6ce5c8d18beb
Yi Wang
67f783fc2eec6ce5c8d18bec
Limin Wang
admin_assigned
2025-04-10T08:53:52.538Z
643d4996482011f5f2be271f
/avatars/134b8f5d44b85d55eaaa2bbe6c409917.svg
limin wang
user
flyacht
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement
  Fine-Tuning
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.
Reinforcement Fine-Tuning with Group Relative Policy Optimization enhances video multimodal large language models, improving spatio-temporal perception without compromising chat abilities.
Group Relative Policy Optimization Reinforcement Fine-Tuning video multimodal large language models spatio-temporal perception temporal grounding object tracking VideoMME MVBench Perception Test