BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs

Junxiao Yang, Jinzhe Tu, Haoran Liu, Xiaoce Wang, Chujie Zheng, Zhexin Zhang, Caishun Chen, Tiantian He, Hongning Wang, Yew-Soon Ong, Minlie Huang

Keywords: Large Reasoning Models, Factual Alignment, Knowledge Boundary

TL;DR: We present BARREL, a framework that enhances the factual reliability of Large Reasoning Models by promoting concise, boundary-aware reasoning, allowing models to stay accurate while using “I don’t know” when uncertain.

Abstract: Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond with “I don’t know”. Instead, they often produce incorrect answers while showing undue confidence, raising concerns about their factual reliability. In this work, we identify two pathological reasoning patterns characterized by overthinking that contribute to the overconfident and incorrect answers: last-minute guessing and second-thought spiraling. To address these issues, we propose BARREL—a novel framework that promotes concise and boundary-aware factual reasoning. Our experiments show that BARREL-training increases the reliability of DeepSeek-R1-Distill-Llama-8B from 39.33% to 61.48%, while still achieving accuracy comparable to models finetuned on reasoning data generated by R1. These results demonstrate that our pilot study is inspiring to build more reliable and factual System 2 LRMs.

Submission Number: 75