Deep Think with Confidence
Large Language Models (LLMs) have shown great potential in reasoning tasks
through test-time scaling methods like self-consistency with majority voting.
However, this approach often leads to diminishing returns in accuracy and high
computational overhead. To address these challenges, we introduce Deep Think
with Confidence (DeepConf), a simple yet powerful method that enhances both
reasoning efficiency and performance at test time. DeepConf leverages
model-internal confidence signals to dynamically filter out low-quality
reasoning traces during or after generation. It requires no additional model
training or hyperparameter tuning and can be seamlessly integrated into
existing serving frameworks. We evaluate DeepConf across a variety of reasoning
tasks and the latest open-source models, including Qwen 3 and GPT-OSS series.
Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up
to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full
parallel thinking.
68a7ca1239413c456c05afc6
Yichao Fu
claimed_verified
2025-08-22T07:22:50.899Z
62ba66296501b0ff15ba1075
/avatars/de468727cb1240fd4b1f24a19fb237a6.svg
Yichao Fu
user
Viol2000
68a7ca1239413c456c05afc7
Xuewei Wang
68a7ca1239413c456c05afc8
Yuandong Tian
admin_assigned
2025-08-22T08:27:28.367Z
6344cf73ee1504dbcd5bdfe7
/avatars/6dd2bf1f9c5679e5c8c85d62c9836aac.svg
Yuandong Tian
user
tydsh
68a7ca1239413c456c05afc9
Jiawei Zhao
claimed_verified
2025-08-22T07:22:52.968Z
64dd8355573d067c9e858262
https://cdn-avatars.huggingface.co/v1/production/uploads/64dd8355573d067c9e858262/Bu2kFs6-lcYc93A_SE8WU.jpeg
Jiawei Zhao
user
jiaweizhao
Deep Think with Confidence
Large Language Models (LLMs) have shown great potential in reasoning tasks
through test-time scaling methods like self-consistency with majority voting.
However, this approach often leads to diminishing returns in accuracy and high
computational overhead. To address these challenges, we introduce Deep Think
with Confidence (DeepConf), a simple yet powerful method that enhances both
reasoning efficiency and performance at test time. DeepConf leverages
model-internal confidence signals to dynamically filter out low-quality
reasoning traces during or after generation. It requires no additional model
training or hyperparameter tuning and can be seamlessly integrated into
existing serving frameworks. We evaluate DeepConf across a variety of reasoning
tasks and the latest open-source models, including Qwen 3 and GPT-OSS series.
Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up
to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full
parallel thinking.
DeepConf enhances reasoning efficiency and performance by filtering low-quality reasoning traces using model-internal confidence signals, achieving high accuracy and reducing token generation.
Deep Think with Confidence DeepConf model-internal confidence signals reasoning traces Qwen 3 GPT-OSS series AIME 2025