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This paper identifies a structural failure mode in token-level credit assignment for LLM reinforcement learning when using LoRA, where intrinsic signals degenerate. It proposes Adapter-Residual Credit Assignment (ARCA), which derives token salience from adapter hidden-state residuals and remains competitive with baselines.
RAGognizer introduces a hallucination-aware fine-tuning approach that integrates a lightweight detection head into LLMs for joint optimization of language modeling and hallucination detection in RAG systems. The paper presents RAGognize, a dataset of naturally occurring closed-domain hallucinations with token-level annotations, and demonstrates state-of-the-art hallucination detection while reducing hallucination rates without degrading language quality.