MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment
Researchers have developed MERIT, a novel two-stage framework designed to improve the assignment of suitable reviewers to academic submissions. The system first trains a reviewer assessor using reinforcement learning, guided by an LLM judge and paper-specific expertise rubrics, to identify and match expertise dimensions. This assessor's predictions are then distilled into an embedding-based retriever for efficient, large-scale assignment. MERIT's 4B reviewer assessor has demonstrated superior performance compared to larger general-purpose LLMs on suitability classification, and its retriever achieves state-of-the-art results on benchmark datasets. AI