Video-MOPD: Multi-Teacher On-Policy Distillation for Video Understanding
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...
| Authors | Zhenxin Qin, Peng Shi, Cong Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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