Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding

• URL: http://arxiv.org/abs/2508.20478v1
• Date: Thu, 28 Aug 2025 06:55:08 GMT
• Title: Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding
• Authors: Yuan Xie, Tianshui Chen, Zheng Ge, Lionel Ni,
• Abstract summary: Video-MTR is a reinforced multi-turn reasoning framework designed to enable iterative key video segment selection and question comprehension.<n>Unlike traditional video reasoning pipeline, which generate predictions in a single turn, Video-MTR performs reasoning in multiple turns.<n>To ensure intermediate reasoning process, we introduce a novel gated bi-level reward system.
• Score: 33.58579390725519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-form video understanding, characterized by long-range temporal dependencies and multiple events, remains a challenge. Existing methods often rely on static reasoning or external visual-language models (VLMs), which face issues like complexity and sub-optimal performance due to the lack of end-to-end training. In this paper, we propose Video-MTR, a reinforced multi-turn reasoning framework designed to enable iterative key video segment selection and question comprehension. Unlike traditional video reasoning pipeline, which generate predictions in a single turn, Video-MTR performs reasoning in multiple turns, selecting video segments progressively based on the evolving understanding of previously processed segments and the current question. This iterative process allows for a more refined and contextually aware analysis of the video. To ensure intermediate reasoning process, we introduce a novel gated bi-level reward system, combining trajectory-level rewards based on answer correctness and turn-level rewards emphasizing frame-query relevance. This system optimizes both video segment selection and question comprehension, eliminating the need for external VLMs and allowing end-to-end training. Extensive experiments on benchmarks like VideoMME, MLVU, and EgoSchema demonstrate that Video-MTR outperforms existing methods in both accuracy and efficiency, advancing the state-of-the-art in long video understanding.

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