Capturing Classic Authorial Style in Long-Form Story Generation with GRPO Fine-Tuning

ArXi:2512.05747v3 Announce Type: replace Evaluating and optimising authorial style in long-form story generation remains challenging because style is often assessed with ad hoc prompting and is frequently conflated with overall writing quality. We propose a two-stage pipeline. First, we train a dedicated style-similarity judge by fine-tuning a sentence-transformer with authorship-verification supervision, and calibrate its similarity outputs into a bounded $[0,1]$ reward.