REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment
Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment. Hallucinations in LRMs arise from two distinct failure sources: reasoning hallucination, where flawed inference steps propagate to an incorrect conclusion, and knowledge hallucination, where the model lacks the requisite factual knowledge to answer the query. To address reasoning hallucination, we propose REIN, an alignment framework that trains LRMs to produce a structured reasoning sequence, $\texttt{<think>} $$\rightarrow$ $\texttt{<reflection>} $$\rightarrow$ $\texttt{<answer>}$, enabling explicit self-reflection before committing to a final answer. To...
| Authors | Zhengze Huang, Luyang Yu, Di Hong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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