title: BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
summary: 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.
paper_authors._id: 682e8c441ffd3af3cb2f27d3
paper_authors.name: Junxiao Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:16:19.492Z
paper_authors._id: 65d859a3661492b25c46a117
paper_authors.avatarUrl: /avatars/06a547a00b472f512a25eef4ddd047bf.svg
paper_authors.fullname: junxiao yang
paper_authors.type: user
paper_authors.user: yangjunxiao2021
paper_authors._id: 682e8c441ffd3af3cb2f27d4
paper_authors.name: Jinzhe Tu
paper_authors._id: 682e8c441ffd3af3cb2f27d5
paper_authors.name: Haoran Liu
paper_authors._id: 682e8c441ffd3af3cb2f27d6
paper_authors.name: Xiaoce Wang
paper_authors._id: 682e8c441ffd3af3cb2f27d7
paper_authors.name: Chujie Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:22:07.693Z
paper_authors._id: 610b70452719facd4ea85e28
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/610b70452719facd4ea85e28/S7nMy7D0Rxq0VIVblhYDG.jpeg
paper_authors.fullname: Chujie Zheng
paper_authors.type: user
paper_authors.user: chujiezheng
paper_authors._id: 682e8c441ffd3af3cb2f27d8
paper_authors.name: Zhexin Zhang
paper_authors._id: 682e8c441ffd3af3cb2f27d9
paper_authors.name: Shiyao Cui
paper_authors._id: 682e8c441ffd3af3cb2f27da
paper_authors.name: Caishun Chen
paper_authors._id: 682e8c441ffd3af3cb2f27db
paper_authors.name: Tiantian He
paper_authors._id: 682e8c441ffd3af3cb2f27dc
paper_authors.name: Hongning Wang
paper_authors._id: 682e8c441ffd3af3cb2f27dd
paper_authors.name: Yew-Soon Ong
paper_authors._id: 682e8c441ffd3af3cb2f27de
paper_authors.name: Minlie Huang
paper_title: BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
paper_summary: 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.
paper_ai_summary: A novel framework, BARREL, addresses overconfidence in Large Reasoning Models by promoting concise and factual reasoning, significantly improving their reliability.
paper_ai_keywords: Large Reasoning Models LRMs overthinking last-minute guessing second-thought spiraling boundary-aware DeepSeek R1-Distill-Llama-8B reliable System 2 LRMs