Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding
Authors: name: Yuan Xie, name: Tianshui Chen, name: Zheng Ge, name: Lionel Ni

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.