title: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
    Language Models
summary: 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.
paper_authors._id: 67eb57023475e7b135788500
paper_authors.name: Qiyuan Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:57:25.339Z
paper_authors._id: 62a42f22c683d02f5b63320c
paper_authors.avatarUrl: /avatars/bc611abe9c4ef8d378123cb8ac9fdbf2.svg
paper_authors.fullname: Qiyuan Zhang
paper_authors.type: user
paper_authors.user: DonJoey
paper_authors._id: 67eb57023475e7b135788501
paper_authors.name: Fuyuan Lyu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:56:19.295Z
paper_authors._id: 65d2bb5c6130ef7be012d235
paper_authors.avatarUrl: /avatars/1c1e3bbb2c683a5c9d1f792a2c13fc4a.svg
paper_authors.fullname: Fuyuan Lyu
paper_authors.type: user
paper_authors.user: silentspring2
paper_authors._id: 67eb57023475e7b135788502
paper_authors.name: Zexu Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T07:46:51.229Z
paper_authors._id: 65d1b42f3da87ce21e33261a
paper_authors.avatarUrl: /avatars/041cb441fa3871acde4ba565632056bf.svg
paper_authors.fullname: RubinSun
paper_authors.type: user
paper_authors.user: RubinSun
paper_authors._id: 67eb57023475e7b135788503
paper_authors.name: Lei Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T08:01:21.676Z
paper_authors._id: 646def60df618b303b419323
paper_authors.avatarUrl: /avatars/97aa761d5255abf230304cfeade87835.svg
paper_authors.fullname: Lei Wang
paper_authors.type: user
paper_authors.user: demolei
paper_authors._id: 67eb57023475e7b135788504
paper_authors.name: Weixu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:56:26.532Z
paper_authors._id: 63414659c5565a4b8d41bc42
paper_authors.avatarUrl: /avatars/25b4ca3002edf4c35cded0902c26632a.svg
paper_authors.fullname: Weixu Zhang
paper_authors.type: user
paper_authors.user: nancy-zwx
paper_authors._id: 67eb57023475e7b135788505
paper_authors.name: Zhihan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:56:31.979Z
paper_authors._id: 63c0c2497f52541dfc7d7567
paper_authors.avatarUrl: /avatars/16c174e2803ef86d09815b36a666ee0e.svg
paper_authors.fullname: ZhihanGUO
paper_authors.type: user
paper_authors.user: ZhihanGUO
paper_authors._id: 67eb57023475e7b135788506
paper_authors.name: Yufei Wang
paper_authors._id: 67eb57023475e7b135788507
paper_authors.name: Irwin King
paper_authors._id: 67eb57023475e7b135788508
paper_authors.name: Xue Liu
paper_authors._id: 67eb57023475e7b135788509
paper_authors.name: Chen Ma
paper_title: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
    Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: test-time scaling test-time computing large language models specialized reasoning tasks open-ended Q&A scaling