Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring

Abstract:Multi-trait essay scoring aims to provide fine-grained evaluation of writing quality across multiple dimensions. However, how to effectively post-train autoregressive scoring models remains underexplored. In this paper, we propose Trait-Aware Policy Optimization (TAPO), a post-training framework tailored to autoregressive multi-trait scoring. Our method decomposes rewards along both the sample and trait dimensions, combining global scoring consistency, trait-level accuracy, format validity, and inter-trait dependency preservation. In addition, we use enhanced prompts throughout training by incorporating original prompt texts and trait descriptions, providing richer semantic information for trait-specific score generation. Experiments across multiple backbone models show that our method consistently improves multi-trait scoring performance over supervised fine-tuning and scalar-reward optimization baselines, demonstrating the effectiveness and transferability of trait-aware post-training for essay scoring.

Computation and Language (cs.CL)

arXiv:2605.25731 [cs.CL]

(or arXiv:2605.25731v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.25731 Focus to learn more arXiv-issued DOI via DataCite

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