CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG

Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention. However, fixed outcome-based rewards do not distinguish incorrect responses with different confidence l