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The paper introduces Open-MOPD, a framework that diagnoses and fixes capability imbalance in multi-teacher on-policy distillation by balancing token-level budgets, improving headroom recovery from 35.6% to 83.4% through dynamic allocation and reward refresh.
MOPD proposes a multi-teacher on-policy distillation paradigm for LLM post-training, enabling efficient integration of multiple domain capabilities by distilling specialized RL teachers into a student model using its own rollouts. It outperforms existing methods like Mix-RL and Cascade RL, and has been deployed in industrial-scale models.