Apple researchers introduce a rubric-based reward framework for open-domain question answering that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions.

The approach improves over the instruction-tuned baseline by 6.5% and over flat rubric variants by 4% across three evaluation axes (composition, grounding, and instruction-following).

Conditioning rubrics on retrieved evidence improves factual support, while decomposing rubrics into quality-specific dimensions further improves coherence, organization, and adherence to query requirements.