A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908426008788992 |
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| author | Ji, Yixin Li, Juntao Xiang, Yang Ye, Hai Wu, Kaixin Yao, Kai Xu, Jia Mo, Linjian Zhang, Min |
| author_facet | Ji, Yixin Li, Juntao Xiang, Yang Ye, Hai Wu, Kaixin Yao, Kai Xu, Jia Mo, Linjian Zhang, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning Ji, Yixin Li, Juntao Xiang, Yang Ye, Hai Wu, Kaixin Yao, Kai Xu, Jia Mo, Linjian Zhang, Min Artificial Intelligence Computation and Language Machine Learning 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. |
| title | A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.02497 |