LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in
  Mechanism via Multi-Step Reasoning
Authors: name: Shenghao Fu; name: Qize Yang; name: Yuan-Ming Li; name: Xihan Wei; name: Xiaohua Xie; name: Wei-Shi Zheng
Published: 2025-09-29 13:43:55

Abstract: Long video understanding is still challenging for recent Large Video-Language
Models (LVLMs) due to the conflict between long-form temporal understanding and
detailed spatial perception. LVLMs with a uniform frame sampling mechanism,
which samples frames with an equal frame size and fixed sampling rate,
inevitably sacrifice either temporal clues or spatial details, resulting in
suboptimal solutions. To mitigate this dilemma, we propose LOVE-R1, a model
that can adaptively zoom in on a video clip. The model is first provided with
densely sampled frames but in a small resolution. If some spatial details are
needed, the model can zoom in on a clip of interest with a large frame
resolution based on its reasoning until key visual information is obtained. The
whole process is implemented as a multi-step reasoning process. To train the
reasoning ability, we first finetune the model on our collected 38k
high-quality CoT data and enhance it with decoupled reinforcement finetuning.
As outcome rewards can not provide fine-grained process supervision, we
decouple multi-step reasoning into multiple single-step reasoning and optimize
the internal zoom-in ability explicitly. Experiments on long video
understanding benchmarks show that our model with the slow-fast adaptive frame
sampling mechanism achieves a great trade-off between sampling density and
frame resolutions, and LOVE-R1 outperforms our baseline Qwen2.5-VL by an
average of 3.1% points across 4 common long video understanding benchmarks.