A new post-training paradigm is emerging in 2026: Multi-Teacher On-Policy Distillation (MOPD), which lets a single model absorb capabilities from multiple specialized RL teachers.
It’s already being used in frontier-scale models like MiMo-V2-Flash, Kimi K3, and DeepSeek-V4, making MOPD an increasingly important technique for combining specialized capabilities into one model.
We have compiled a list of popular papers tracing the development of MOPD from its earliest forms to its latest iterations.
Check it out now:
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