Researchragreinforcement learningevidence groundingabstention
GRACE Trains RAG Models With Abstention
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9.0
Researchers introduce GRACE, a reinforcement-learning framework (submitted Jan. 8, 2026) that jointly enforces evidence-based grounding and reliable abstention in Retrieval-Augmented Generation (RAG) systems. GRACE uses heterogeneous retrievers to construct training data and a multi-stage gated reward to teach models to assess evidence sufficiency, extract supporting passages, and answer or abstain. Experiments on two benchmarks show state-of-the-art accuracy and a 10% annotation cost.
Scoring Rationale
Strong innovation and practical gains across RAG systems, tempered by single-source academic submission lacking wide validation.
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