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D$^3$-MOPD: Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation
Multi-teacher on-policy distillation (MOPD) distills several domain-expert teachers into a single student by minimizing per-domain reverse-KL divergence on the student's own rollouts. Existing approaches typically fix the per-domain data mixture before training, overlooking the fact that different d
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