[Submitted on 8 Sep 2026]
Title:Video-MOPD: Multi-Teacher On-Policy Distillation for Video Understanding
View a PDF of the paper titled Video-MOPD: Multi-Teacher On-Policy Distillation for Video Understanding, by Zhenxin Qin and 5 other authors
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Abstract:Video understanding demands a convergence of complementary capabilities across perception, temporal understanding, and complex reasoning, which are difficult to jointly optimize within a single model. We introduce Video-MOPD-8B, an open-weight model dedicated to video understanding tasks. To fundamentally enhance its capabilities, we conduct targeted reinforcement learning (RL) optimization across three core domains: video temporal grounding (VTG), general video comprehension, and video STEM reasoning. We then unify their complementary capabilities via Multi-Teacher On-Policy Distillation (MOPD), which consolidates expert knowledge by supervising student-generated trajectories with routed teacher feedback. We further introduce Reliability-Aware Informative Sampling (RAIS), which selects examples with consistently reliable teacher supervision and large teacher-student performance gaps. Together, these components enable Video-MOPD-8B to achieve coordinated and comprehensive performance gains across diverse video understanding tasks. Extensive experiments on comprehensive benchmarks covering general video understanding, temporal grounding, video reasoning, and video STEM tasks demonstrate that Video-MOPD-8B achieves state-of-the-art performance among existing models at a comparable scale. The trained model weights are available at this https URL.
Comments: Technical report
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09300 [cs.CV]
(or arXiv:2609.09300v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.09300
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhenxin Qin [view email] [v1] Tue, 8 Sep 2026 18:00:22 UTC (1,708 KB)
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