What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
  Language Models
Authors: name: Qiyuan Zhang, name: Fuyuan Lyu, name: Zexu Sun, name: Lei Wang, name: Weixu Zhang, name: Zhihan Guo, name: Yufei Wang, name: Irwin King, name: Xue Liu, name: Chen Ma

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.