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