Abstract
This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, an active, reasoning-equipped multimodal large language model (MLLM) agent designed for efficient video context navigation, avoiding the redundancy of exhaustive search. At the core of LongVideo-R1 lies a reasoning module that leverages high-level visual cues to infer the most informative video clip for subsequent processing. During inference, the agent initiates traversal from top-level visual summaries and iteratively refines its focus, immediately halting the exploration process upon acquiring sufficient knowledge to answer the query. To facilitate training, we first extract hierarchical video captions from CGBench, a video corpus with grounding annotations, and guide GPT-5 to generate 33K high-quality chain-of-thought-with-tool trajectories. The LongVideo-R1 agent is fine-tuned upon the Qwen-3-8B model through a two-stage paradigm: supervised fine-tuning (SFT) followed by reinforcement learning (RL), where RL employs a specifically designed reward function to maximize selective and efficient clip navigation. Experiments on multiple long video benchmarks validate the effectiveness of name, which enjoys superior tradeoff between QA accuracy and efficiency. All curated data and source code are provided in the supplementary material and will be made publicly available. Code and data are available at: this https URL
Abstract (translated)
这篇论文解决了低计算预算下长视频理解这一关键且未充分探索的挑战。我们提出了LongVideo-R1，这是一种装备有推理能力的多模态大型语言模型（MLLM）代理，旨在实现高效的视频上下文导航，并避免详尽搜索带来的冗余问题。LongVideo-R1的核心在于一个推理模块，该模块利用高层次视觉线索来推断后续处理中最具信息量的视频片段。在进行推理时，代理从顶级视觉概览开始遍历，迭代地聚焦其关注点，并在获取足够知识以回答查询后立即停止探索过程。 为了方便训练，我们首先从CGBench（一个带有定位标注的视频语料库）中提取分层视频字幕，并指导GPT-5生成33K条高质量的链式思维与工具轨迹。LongVideo-R1代理通过两阶段范式在Qwen-3-8B模型上进行微调：第一阶段为监督微调（SFT），随后是强化学习（RL）。其中，RL采用专门设计的奖励函数来最大化选择性和高效的片段导航。实验表明，在多个长视频基准测试中，该方法在准确性和效率之间享有优越的权衡。所有整理好的数据和源代码都包含在补充材料中，并将公开发布。 代码和数据可在以下链接获得：this https URL
URL
https://arxiv.org/abs/2602.20913
