Video-Thinker: Sparking "Thinking with Videos" via Reinforcement
  Learning
Authors: name: Shijian Wang, name: Jiarui Jin, name: Xingjian Wang, name: Linxin Song, name: Runhao Fu, name: Hecheng Wang, name: Zongyuan Ge, name: Yuan Lu, name: Xuelian Cheng

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.