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

Yuan Xie; Tianshui Chen; Zheng Ge; Lionel Ni

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arXiv · 2508.20478

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

Yuan Xie·Tianshui Chen·Zheng Ge·Lionel Ni

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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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BibTeX RIS

Yuan Xie, Tianshui Chen, Zheng Ge, Lionel Ni. 2026-05-29. Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding. https://arxiv.org/abs/2508.20478

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