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)

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. However, their proficiency and reliability in the specialized domain of financial data analysis, particularly focusing on data-driven thinking, remain uncertain. To bridge this gap, we introduce FinDABench, a comprehensive benchmark designed to evaluate the financial data analysis capabilities of LLMs within this context. The benchmark comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. FinDABench assesses LLMs across three dimensions: 1) Core Ability, evaluating the models’ ability to perform financial indicator calculation and corporate sentiment risk assessment; 2) Analytical Ability, determining the models’ ability to quickly comprehend textual information and analyze abnormal financial reports; and 3) Technical Ability, examining the models’ use of technical knowledge to address real-world data analysis challenges involving analysis generation and charts visualization from multiple perspectives. We will release FinDABench, and the evaluation scripts at https://github.com/xxx. FinDABench aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in the field of financial data analysis.