Automated Essay Scoring (AES) in low-resource, non-English educational settings faces a compounded challenge: existing corpora record only a final holistic score, discarding the rich intermediate reasoning that human examiners rely on. We argue this annotation gap may contribute to Rubric Drift—the observed tendency of LLMs to apply scoring norms misaligned with the target curriculum, potentially reflecting Anglocentric defaults. To address this, we introduce MPEAB-2430, a multilingual AES benchmark spanning Malay, Hindi, and Arabic curricula, annotated with Pedagogical Chain-of-Thought (P-CoT) trajectories that capture sub-dimensional analyses, explicit error attribution, and curriculum-conformant feedback, derived entirely from independent, language-specific expert practice. Leveraging this rich but modest-scale supervision, we propose RL-CRAF++, a sample-efficient pipeline combining CoT-SFT with Constrained GRPO. Crucially, our RL formulation relies entirely on human-annotation-derived rewards, completely bypassing the potential biases of external LLM judges. Evaluated independently per language, a 4B parameter RL-CRAF++ model consistently surpasses zero-shot frontier models (including GPT-5 and DeepSeek-V3.2) on in-domain test sets. Ablation analysis reveals that P-CoT supervision accounts for approximately 78.1% of the alignment gain over standard SFT, providing strong evidence that, in this culturally situated evaluation setting, process-level structural supervision contributes substantially more than RL-stage calibration alone. Our code is available at https://github.com/TW-NLP/MPEAB

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)