Chinese elementary-school writing is rubric-driven. We study it as a teacher-facing instructional support task: given a classroom prompt and requirements, generate a teacher-reviewable example matching the target genre, rubric, length, and age-appropriate register. Directly prompted frontier LLMs often mis-route genres or adopt an adult register.

We present **RA-MOPD**, a four-stage pipeline that post-trains a 4B model into a unified essay writer across four genres and eight rubric dimensions. Its core stage augments on-policy reverse-KL distillation with *Rubric-Weighted KL*, which up-weights rubric-critical tokens via reward-model attribution, and *Cross-Genre Mixing*, a parameter-free expert blend guided by an empirical transfer prior.

On a new 6,000-essay benchmark with eight reward models (QWK = 0.864), RA-MOPD reaches 4.21/5.00 and 97.8% routing—within 0.09 of GPT-5.3 3-shot (p < 0.01). No statistically significant difference is detected relative to a 4×-cost four-expert logit-averaging baseline (Δ = +0.03, p = 0.18).

A GPT-4.1 judge, BERTScore, a 1,200-essay teacher study (62.8% preference), and register analysis provide converging evidence beyond RM metrics. GPT-5.5 holistic evaluation further supports the component-level gains, which remain stable on Qwen3-1.7B without model-specific retuning.

Code and data: [https://github.com/TW-NLP/MOPD](https://github.com/TW-NLP/MOPD)