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A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning

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arxiv 2501.02497 v3 pith:CCFJREH6 submitted 2025-01-05 cs.AI cs.CLcs.LG

Yixin Ji , Juntao Li , Yang Xiang , Hai Ye , Kaixin Wu , Kai Yao , Jia Xu , Linjian Mo
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Min Zhang
This is my paper
classification cs.AIcs.CLcs.LG
keywords computemodelstest-timesystem-2system-1modelreasoningcomplex
verification ladder T0 review T1 audit T2 compute T3 formal
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The remarkable performance of the o1 model in complex reasoning demonstrates that test-time compute scaling can further unlock the model's potential, enabling powerful System-2 thinking. However, there is still a lack of comprehensive surveys for test-time compute scaling. We trace the concept of test-time compute back to System-1 models. In System-1 models, test-time compute addresses distribution shifts and improves robustness and generalization through parameter updating, input modification, representation editing, and output calibration. In System-2 models, it enhances the model's reasoning ability to solve complex problems through repeated sampling, self-correction, and tree search. We organize this survey according to the trend of System-1 to System-2 thinking, highlighting the key role of test-time compute in the transition from System-1 models to weak System-2 models, and then to strong System-2 models. We also point out advanced topics and future directions.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling

    cs.AI 2026-04 unverdicted novelty 6.0 of 10

    A disagreement-guided routing framework dynamically selects among resolution, voting, and rewriting strategies for test-time scaling, delivering 3-7% accuracy gains with lower sampling cost on mathematical benchmarks.

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  3. R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems

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    R4ec trains a small reflection model to critique and refine LLM-generated user and item knowledge, which then improves downstream recommendation accuracy.

  4. Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models

    cs.CL 2025-07 reject novelty 4.0 of 10

    A step-level verifier-guided hybrid of Best-of-N sampling, Monte Carlo tree search, and conditional self-refinement improves reasoning in small instruction-tuned LLMs, claiming up to 28.6-point gains.

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