Brief

Researchers have introduced Trait-Aware Policy Optimization (TAPO), a novel post-training framework designed to enhance autoregressive models for multi-trait essay scoring. This method decomposes rewards across samples and traits, integrating global consistency, trait accuracy, and inter-trait dependencies. Experiments indicate that TAPO significantly improves scoring performance compared to standard supervised fine-tuning and scalar-reward optimization techniques. AI