Background: modern large-model post-training almost always relies on reinforcement learning to squeeze out specific capabilities, but stuffing code, math, reasoning, and conversation demands into a single model inevitably sacrifices one for another. Existing Off-Policy Finetune, Mix-RL, Cascade RL, and Param-Merge approaches are either inefficient or lose points in some domain — weights and data are coupled too deeply, making parallel engineering progress hard. Core method: Xiaomi's LLM-Core team, together with Peking University, has published MOPD (Multi-Teacher On-Policy Distillation) in arXiv:2606.30406, taking a different tack — first run RL independently for each domain to get a set of domain teachers, then let the student, on its own generated rollouts, use a token-level stop-gradient distillation advantage Â_t = sg[log π_teacher(t) - log π_student(t)] to update backwards. This single move solves two problems at once: eliminates exposure bias (the student learns on tokens encountered on its own real-distribution rollouts, no longer on out-of-distribution samples from offline datasets); and preserves a dense optimization signal (token-level differences are available immediately, no need to wait for the end-of-episode return). Numerical results: on Qwen3-30B-A3B, MOPD comprehensively beats Mix-RL, Cascade RL, Off-Policy Finetune, and Param-Merge baselines, inheriting 91–95% of each teacher's capability, and multiple domain teachers can be developed in parallel and independently, significantly improving engineering efficiency. Deployment: MOPD has already been deployed at scale in Xiaomi's MiMo-V2-Flash technical report; recently Upstage's Solar Open 2 (250B total / 15B active) in Korea used it directly in agent training; NVIDIA NeMo-RL has integrated MOPD as a native algorithm in its nightly build, with advantage estimator set to opd, running async GRPO + NeMo Gym on agent multi-turn interaction pipelines. Commentary: many people in the past equated on-policy distillation with "training the student on teacher logits"; MOPD redefines it as "using the teacher's per-token probability difference on the student's own rollouts to construct an advantage". This is essentially reverse-KL minimization, but without needing the teacher to output the full vocabulary — much more engineering-friendly. For Chinese agent teams, this means they can raise several specialized teachers in parallel and then distill a unified base model, rather than painfully doing model merging.