LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in
  Mechanism via Multi-Step Reasoning
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.
68db43edd2bf1f4b15ec734e
Shenghao Fu
claimed_verified
2025-09-30T06:30:44.180Z
67067633351e0c16a5c27497
/avatars/356aa3431198c8931b820a714bcfb19d.svg
Shenghao Fu
user
fushh7
68db43edd2bf1f4b15ec734f
Qize Yang
claimed_verified
2025-09-30T10:18:08.655Z
66a097801a26a2350395edc7
/avatars/1e7e127cb7222df7d56e5bfda6bab519.svg
Qize Yang
user
PhilipC
68db43edd2bf1f4b15ec7350
Yuan-Ming Li
claimed_verified
2025-09-30T06:30:39.978Z
644fe6a9e1d7a97f3b66e906
/avatars/ad1a45f0b1c8a4d03ba87f2a3ce5a8f8.svg
Yuanming-Li
user
Lymann
68db43edd2bf1f4b15ec7351
Xihan Wei
68db43edd2bf1f4b15ec7352
Xiaohua Xie
68db43edd2bf1f4b15ec7353
Wei-Shi Zheng
LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in
  Mechanism via Multi-Step Reasoning
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.
LOVE-R1, a model with adaptive frame sampling, enhances long video understanding by balancing temporal and spatial details through multi-step reasoning and decoupled reinforcement learning.
Large Video-Language Models LVLMs uniform frame sampling adaptive zoom densely sampled frames large frame resolution multi-step reasoning decoupled reinforcement finetuning slow-fast adaptive frame sampling