Generating Constructive Feedback on Stories via Reinforcement Learning

The paper introduces a reinforcement learning method to improve the quality of feedback generated by large language models for creative writers. By training with group relative policy optimization and a multi‑component reward that emphasizes tailored, actionable, and critical‑issue‑focused feedback, the authors demonstrate that their approach outperforms existing LLMs and baselines on three story corpora. The study shows that actionable suggestions are the key factor driving constructive feedback.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• reinforcement-learning
• benchmarks