PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while maintaining diagnostic correctness remains challenging. We ask how can we update an LLM for automated feedback generation to align with a target instructors style without sacrificing core knowledge? We study how Reinforcement Learning from Human Feedback (RLHF) can adapt a transformer-based LLM to generate programming feedback that matches a professors grading voice. We introduce PERSA, an RLHF pipeline that combines supervised fine-tuning on professor demonstrations, reward modeling from pairwise preferences, and Proximal Policy Optimizati...
| Authors | Ravi Ranjan, Utkarsh Grover, Xiaomin Lin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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