What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
  Language Models
As enthusiasm for scaling computation (data and parameters) in the
pretraining era gradually diminished, test-time scaling (TTS), also referred to
as ``test-time computing'' has emerged as a prominent research focus. Recent
studies demonstrate that TTS can further elicit the problem-solving
capabilities of large language models (LLMs), enabling significant
breakthroughs not only in specialized reasoning tasks, such as mathematics and
coding, but also in general tasks like open-ended Q&A. However, despite the
explosion of recent efforts in this area, there remains an urgent need for a
comprehensive survey offering a systemic understanding. To fill this gap, we
propose a unified, multidimensional framework structured along four core
dimensions of TTS research: what to scale, how to scale, where to scale, and
how well to scale. Building upon this taxonomy, we conduct an extensive review
of methods, application scenarios, and assessment aspects, and present an
organized decomposition that highlights the unique functional roles of
individual techniques within the broader TTS landscape. From this analysis, we
distill the major developmental trajectories of TTS to date and offer hands-on
guidelines for practical deployment. Furthermore, we identify several open
challenges and offer insights into promising future directions, including
further scaling, clarifying the functional essence of techniques, generalizing
to more tasks, and more attributions.
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Qiyuan Zhang
admin_assigned
2025-04-01T07:57:25.339Z
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Qiyuan Zhang
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DonJoey
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Fuyuan Lyu
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2025-04-01T07:56:19.295Z
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Fuyuan Lyu
user
silentspring2
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Zexu Sun
claimed_verified
2025-04-01T07:46:51.229Z
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RubinSun
user
RubinSun
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Lei Wang
claimed_verified
2025-04-01T08:01:21.676Z
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Lei Wang
user
demolei
67eb57023475e7b135788504
Weixu Zhang
admin_assigned
2025-04-01T07:56:26.532Z
63414659c5565a4b8d41bc42
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Weixu Zhang
user
nancy-zwx
67eb57023475e7b135788505
Zhihan Guo
admin_assigned
2025-04-01T07:56:31.979Z
63c0c2497f52541dfc7d7567
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ZhihanGUO
user
ZhihanGUO
67eb57023475e7b135788506
Yufei Wang
67eb57023475e7b135788507
Irwin King
67eb57023475e7b135788508
Xue Liu
67eb57023475e7b135788509
Chen Ma
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
  Language Models
As enthusiasm for scaling computation (data and parameters) in the
pretraining era gradually diminished, test-time scaling (TTS), also referred to
as ``test-time computing'' has emerged as a prominent research focus. Recent
studies demonstrate that TTS can further elicit the problem-solving
capabilities of large language models (LLMs), enabling significant
breakthroughs not only in specialized reasoning tasks, such as mathematics and
coding, but also in general tasks like open-ended Q&A. However, despite the
explosion of recent efforts in this area, there remains an urgent need for a
comprehensive survey offering a systemic understanding. To fill this gap, we
propose a unified, multidimensional framework structured along four core
dimensions of TTS research: what to scale, how to scale, where to scale, and
how well to scale. Building upon this taxonomy, we conduct an extensive review
of methods, application scenarios, and assessment aspects, and present an
organized decomposition that highlights the unique functional roles of
individual techniques within the broader TTS landscape. From this analysis, we
distill the major developmental trajectories of TTS to date and offer hands-on
guidelines for practical deployment. Furthermore, we identify several open
challenges and offer insights into promising future directions, including
further scaling, clarifying the functional essence of techniques, generalizing
to more tasks, and more attributions.
A survey of test-time scaling for large language models, structured into dimensions of what, how, where, and how well to scale, with an emphasis on its applications and future challenges.
test-time scaling test-time computing large language models specialized reasoning tasks open-ended Q&A scaling