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Inference-time scaling has proven effective in boosting large language model (LLM) performance through increased test-time computation.
Some aspects of the sequential design of experiments
Herbert Robbins · 1952
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Making language models better reasoners with step-aware verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2023
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Alphazero-like tree-search can guide large language model decoding and training
Xidong Feng, Ziyu Wan, Muning Wen, Ying Wen, Weinan Zhang, and Jun Wang · 2023
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Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with llms
Pranjal Aggarwal, Aman Madaan, Yiming Yang, et al · 2023
Earlier work this paper cites.
From decoding to meta-generation: Inference-time algorithms for large language models
Sean Welleck, Amanda Bertsch, Matthew Finlayson, Hailey Schoelkopf, Alex Xie, Graham Neubig, Ilia Kulikov, and Zaid Harchaoui · 2024
Earlier work this paper cites.
Are more LLM calls all you need? towards the scaling properties of compound AI systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, and James Zou · 2024
Earlier work this paper cites.
Theoretical guarantees on the best-of-n alignment policy
Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D’Amour, Jacob Eisenstein, Chirag Nagpal, and Ananda Theertha Suresh · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Scaling inference computation: Compute-optimal inference for problem-solving with language models
Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang · 2024
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Beyond chinchilla-optimal: Accounting for inference in language model scaling laws
Nikhil Sardana, Jacob Portes, Sasha Doubov, and Jonathan Frankle · 2024
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Scaling llm inference with optimized sample compute allocation
Think deep, think fast: Investigating efficiency of verifier-free inference-time-scaling methods
Junlin Wang, Shang Zhu, Jon Saad-Falcon, Ben Athiwaratkun, Qingyang Wu, Jue Wang, Shuaiwen Leon Song, Ce Zhang, Bhuwan Dhingra, and James Zou · 2025
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Scalable best-of-n selection for large language models via self-certainty
Zhewei Kang, Xuandong Zhao, and Dawn Song · 2025
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Sets: Leveraging self-verification and self-correction for improved test-time scaling
Jiefeng Chen, Jie Ren, Xinyun Chen, Chengrun Yang, Ruoxi Sun, and Sercan Ö Arık · 2025
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Disc: Dynamic decomposition improves llm inference scaling
Jonathan Light, Wei Cheng, Wu Yue, Masafumi Oyamada, Mengdi Wang, Santiago Paternain, and Haifeng Chen · 2025
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Kexun Zhang, Shang Zhou, Danqing Wang, William Yang Wang, and Lei Li · 2024
Cited alongside, same era.
Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation
Rohin Manvi, Anikait Singh, and Stefano Ermon · 2024
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Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al · 2024
Cited alongside, same era.
When hindsight is not 20/20: Testing limits on reflective thinking in large language models
Yanhong Li, Chenghao Yang, and Allyson Ettinger · 2024
Cited alongside, same era.
Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning
Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2025
Cited alongside, same era.
Can 1b llm surpass 405b llm? rethinking compute-optimal test-time scaling
Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, and Bowen Zhou
Cited in the paper.
Metascale: Test-time scaling with evolving meta-thoughts
Qin Liu, Wenxuan Zhou, Nan Xu, James Y Huang, Fei Wang, Sheng Zhang, Hoifung Poon, and Muhao Chen
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Mehul Damani, Idan Shenfeld, Andi Peng, Andreea Bobu, and Jacob Andreas · 2025
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Think smarter not harder: Adaptive reasoning with inference aware optimization
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Qwen3, April 2025
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