Revisiting the Test-Time Scaling of o1-like Models: Do they Truly
  Possess Test-Time Scaling Capabilities?
The advent of test-time scaling in large language models (LLMs), exemplified
by OpenAI's o1 series, has advanced reasoning capabilities by scaling
computational resource allocation during inference. While successors like QwQ,
Deepseek-R1 (R1) and LIMO replicate these advancements, whether these models
truly possess test-time scaling capabilities remains underexplored. This study
found that longer CoTs of these o1-like models do not consistently enhance
accuracy; in fact, correct solutions are often shorter than incorrect ones for
the same questions. Further investigation shows this phenomenon is closely
related to models' self-revision capabilities - longer CoTs contain more
self-revisions, which often lead to performance degradation. We then compare
sequential and parallel scaling strategies on QwQ, R1 and LIMO, finding that
parallel scaling achieves better coverage and scalability. Based on these
insights, we propose Shortest Majority Vote, a method that combines parallel
scaling strategies with CoT length characteristics, significantly improving
models' test-time scalability compared to conventional majority voting
approaches.
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Zhiyuan Zeng
67b56007fa141a55e51d9d79
Qinyuan Cheng
claimed_verified
2025-06-24T08:14:51.810Z
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QinyuanCheng
user
Cqy2019
67b56007fa141a55e51d9d7a
Zhangyue Yin
admin_assigned
2025-02-19T11:50:17.993Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1653366416287-628c5da32f09ccf530204dbe.jpeg
Zhangyue Yin
user
yinzhangyue
67b56007fa141a55e51d9d7b
Yunhua Zhou
67b56007fa141a55e51d9d7c
Xipeng Qiu
admin_assigned
2025-02-19T11:50:29.919Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1632381702899-61457b8deff2c9fdb4de4988.jpeg
Xipeng Qiu
user
xpqiu
Revisiting the Test-Time Scaling of o1-like Models: Do they Truly
  Possess Test-Time Scaling Capabilities?
The advent of test-time scaling in large language models (LLMs), exemplified
by OpenAI's o1 series, has advanced reasoning capabilities by scaling
computational resource allocation during inference. While successors like QwQ,
Deepseek-R1 (R1) and LIMO replicate these advancements, whether these models
truly possess test-time scaling capabilities remains underexplored. This study
found that longer CoTs of these o1-like models do not consistently enhance
accuracy; in fact, correct solutions are often shorter than incorrect ones for
the same questions. Further investigation shows this phenomenon is closely
related to models' self-revision capabilities - longer CoTs contain more
self-revisions, which often lead to performance degradation. We then compare
sequential and parallel scaling strategies on QwQ, R1 and LIMO, finding that
parallel scaling achieves better coverage and scalability. Based on these
insights, we propose Shortest Majority Vote, a method that combines parallel
scaling strategies with CoT length characteristics, significantly improving
models' test-time scalability compared to conventional majority voting
approaches.
Shortest Majority Vote enhances test-time scalability in large language models by leveraging parallel scaling and optimal CoT lengths.
test-time scaling large language models (LLMs) O1 series QwQ Deepseek-R1 (R1) LIMO CoTs self-revision capabilities sequential scaling parallel scaling