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

AbstractEvaluating and optimizing authorial style in long-form story generation is challenging because style judgments often rely on subjective human voting, and there is no stable automatic evaluation method. We propose a two-stage pipeline. First, we train a style-similarity judge by fine-tuning a sentence-transformer with authorship-verification supervision, and calibrate its similarity outputs into a bounded [0,1] reward. Second, we use this judge as the primary reward in Group Relative Policy Optimization (GRPO) to fine-tune an 8B story generator for style-conditioned writing, avoiding the accept/reject supervision required by Direct Preference Optimization (DPO). Across four target authors (Mark Twain, Jane Austen, Charles Dickens, Thomas Hardy), the GRPO-trained 8B model achieves higher style scores than open-weight baselines, with an average style score of 0.893 across authors. These results suggest that AV-calibrated reward modeling provides a practical mechanism for controllable long-form style transfer under moderate model size and training budget.