Zihao Fan

In joint audio-video generation, audio has far fewer tokens than video, so its supervision is sparse and distillation is unstable, and simply raising the audio loss weight makes things worse. OmniOPD has the audio teacher produce targets from a cleaner visual condition, samples several trajectories per prompt, and weights each timestep by teacher-student transition divergence; on JavisBench it raises video quality VQ from 2.714 for LTX-2.3 to 3.347 while holding audio quality AQ at 5.449, below the audio teacher's 5.576.

In joint audio-video generation, audio has far fewer tokens than video, so its supervision is sparse and distillation is unstable, and simply raising the audio loss weight makes things worse. OmniOPD has the audio teacher produce targets from a cleaner visual condition, samples several trajectories per prompt, and weights each timestep by teacher-student transition divergence; on JavisBench it raises video quality VQ from 2.714 for LTX-2.3 to 3.347 while holding audio quality AQ at 5.449, below the audio teacher's 5.576.