EvidenceRL uses reinforcement learning (GRPO) to explicitly optimize for evidence adherence, reducing hallucinations in high-stakes RAG pipelines.
March 23, 2026
Original Paper
EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Models
arXiv · 2603.19532
The Takeaway
Instead of relying on prompt engineering or standard SFT, this directly penalizes ungrounded claims during training. The 5x reduction in hallucinations in medical and legal domains makes it a highly practical framework for deploying LLMs in critical production environments.
From the abstract
Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available evidence. This failure is especially problematic in high-stakes domains where decisions must be justified by verifiable information. We introduce \textbf{EvidenceRL}, a reinforcement learning framework that enforces evidence adherence during training. EvidenceRL scores candidate responses for grounding (entailment with retrieved evidence and context) and c