Video-Thinker: Sparking "Thinking with Videos" via Reinforcement
  Learning
Recent advances in image reasoning methods, particularly "Thinking with
Images", have demonstrated remarkable success in Multimodal Large Language
Models (MLLMs); however, this dynamic reasoning paradigm has not yet been
extended to video reasoning tasks. In this paper, we propose Video-Thinker,
which empowers MLLMs to think with videos by autonomously leveraging their
intrinsic "grounding" and "captioning" capabilities to generate reasoning clues
throughout the inference process. To spark this capability, we construct
Video-Thinker-10K, a curated dataset featuring autonomous tool usage within
chain-of-thought reasoning sequences. Our training strategy begins with
Supervised Fine-Tuning (SFT) to learn the reasoning format, followed by Group
Relative Policy Optimization (GRPO) to strengthen this reasoning capability.
Through this approach, Video-Thinker enables MLLMs to autonomously navigate
grounding and captioning tasks for video reasoning, eliminating the need for
constructing and calling external tools. Extensive experiments demonstrate that
Video-Thinker achieves significant performance gains on both in-domain tasks
and challenging out-of-domain video reasoning benchmarks, including
Video-Holmes, CG-Bench-Reasoning, and VRBench. Our Video-Thinker-7B
substantially outperforms existing baselines such as Video-R1 and establishes
state-of-the-art performance among 7B-sized MLLMs.
6900b67f646208eac0d1efe9
Shijian Wang
claimed_verified
2025-10-30T14:43:40.824Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QTh1c-okqsl-NLHatc8Gu.png
Shijian Wang
ShijianW01
user
ShijianW01
6900b67f646208eac0d1efea
Jiarui Jin
claimed_verified
2025-10-30T14:42:57.692Z
646cc5add2cc138217dbe133
https://cdn-avatars.huggingface.co/v1/production/uploads/646cc5add2cc138217dbe133/Uzvt9_UWIRIT4t4gHdjid.jpeg
Jiarui Jin
jinjiarui97
user
jinjiarui97
6900b67f646208eac0d1efeb
Xingjian Wang
6900b67f646208eac0d1efec
Linxin Song
6900b67f646208eac0d1efed
Runhao Fu
6900b67f646208eac0d1efee
Hecheng Wang
6900b67f646208eac0d1efef
Zongyuan Ge
6900b67f646208eac0d1eff0
Yuan Lu
6900b67f646208eac0d1eff1
Xuelian Cheng
Video-Thinker: Sparking "Thinking with Videos" via Reinforcement
  Learning
Recent advances in image reasoning methods, particularly "Thinking with
Images", have demonstrated remarkable success in Multimodal Large Language
Models (MLLMs); however, this dynamic reasoning paradigm has not yet been
extended to video reasoning tasks. In this paper, we propose Video-Thinker,
which empowers MLLMs to think with videos by autonomously leveraging their
intrinsic "grounding" and "captioning" capabilities to generate reasoning clues
throughout the inference process. To spark this capability, we construct
Video-Thinker-10K, a curated dataset featuring autonomous tool usage within
chain-of-thought reasoning sequences. Our training strategy begins with
Supervised Fine-Tuning (SFT) to learn the reasoning format, followed by Group
Relative Policy Optimization (GRPO) to strengthen this reasoning capability.
Through this approach, Video-Thinker enables MLLMs to autonomously navigate
grounding and captioning tasks for video reasoning, eliminating the need for
constructing and calling external tools. Extensive experiments demonstrate that
Video-Thinker achieves significant performance gains on both in-domain tasks
and challenging out-of-domain video reasoning benchmarks, including
Video-Holmes, CG-Bench-Reasoning, and VRBench. Our Video-Thinker-7B
substantially outperforms existing baselines such as Video-R1 and establishes
state-of-the-art performance among 7B-sized MLLMs.
Video-Thinker, a multimodal large language model, autonomously reasons with videos using intrinsic grounding and captioning capabilities, achieving state-of-the-art performance on various video reasoning benchmarks.
Thinking with Images Multimodal Large Language Models Video-Thinker Video-Thinker-10K Supervised Fine-Tuning Group Relative Policy Optimization Video-Holmes CG-Bench-Reasoning VRBench Video-R1