From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers

Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes

Original reporting by Apple Machine Learning Research. GridIndex is an aggregation and intelligence layer — full credit to the original publisher.