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Recent studies have demonstrated the effectiveness of LLM test-time scaling.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909
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Buy 4 reinforce samples, get a baseline for free!
Wouter Kool, Herke van Hoof, and Max Welling. 2019 · 2019
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
Earlier work this paper cites.
Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective
Tom Everitt, Marcus Hutter, Ramana Kumar, and Victoria Krakovna. 2021 · 2021
Earlier work this paper cites.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
Earlier work this paper cites.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
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Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, et al. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 · 2022
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Amc 2023
AI-MO. 2024b · 2023
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Leftover lunch: Advantage-based offline reinforcement learning for language models
Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, and Mark Riedl. 2023 · 2023
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The reversal curse: Llms trained on" a is b" fail to learn" b is a"
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. 2023 · 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 · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton. 2023 · 2023
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Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2023 · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
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Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. 2023 · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
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Llms cannot find reasoning errors, but can correct them!
Gladys Tyen, Hassan Mansoor, Peter Chen, Tony Mak, and Victor Cărbune. 2023 · 2023
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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 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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Qwen2 technical report
2024 · 2024
Cited alongside, same era.
Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms
Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker. 2024 · 2024
Cited alongside, same era.
Aime 2024
AI-MO. 2024a · 2024
Cited alongside, same era.
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 · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Cited alongside, same era.
Rewarding progress: Scaling automated process verifiers for llm reasoning
Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. 2024 · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Preference fine-tuning of llms should leverage suboptimal, on-policy data
Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, and Aviral Kumar. 2024 · 2024
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Cruxeval: A benchmark for code reasoning, understanding and execution
Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I. Wang. 2024 · 2024
Cited alongside, same era.
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, et al. 2024 · 2024
Cited alongside, same era.
Huchen Jiang, Yangyang Ma, Chaofan Ding, Kexin Luan, and Xinhan Di. 2024 · 2024
Cited alongside, same era.
When can llms actually correct their own mistakes? a critical survey of self-correction of llms
Ryo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, and Rui Zhang. 2024 · 2024
Cited alongside, same era.
Training language models to self-correct via reinforcement learning
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al. 2024 · 2024
Cited alongside, same era.
Numinamath
Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu. 2024 · 2024
Cited alongside, same era.
Mario: Math reasoning with code interpreter output–a reproducible pipeline
Minpeng Liao, Wei Luo, Chengxi Li, Jing Wu, and Kai Fan. 2024 · 2024
Cited alongside, same era.
Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024
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Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data
Shubham Toshniwal, Wei Du, Ivan Moshkov, Branislav Kisacanin, Alexan Ayrapetyan, and Igor Gitman. 2024 · 2024
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An empirical analysis of compute-optimal inference for problem-solving with language models
Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang. 2024 · 2024
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Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement
An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, et al. 2024 · 2024
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Free process rewards without process labels
Lifan Yuan, Wendi Li, Huayu Chen, Ganqu Cui, Ning Ding, Kaiyan Zhang, Bowen Zhou, Zhiyuan Liu, and Hao Peng. 2024 · 2024
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Marco-o1: Towards open reasoning models for open-ended solutions
Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2024 · 2024
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Processbench: Identifying process errors in mathematical reasoning
Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2024 · 2024
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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 · 2025
Closest in time.
Self-playing adversarial language game enhances llm reasoning
Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Yong Dai, Lei Han, Xiaolong Li, et al. 2025 · 2025
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Process reinforcement through implicit rewards
Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, et al. 2025 · 2025
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rstar-math: Small llms can master math reasoning with self-evolved deep thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang. 2025 · 2025
Closest in time.
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 · 2025
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Recursive introspection: Teaching language model agents how to self-improve
Yuxiao Qu, Tianjun Zhang, Naman Garg, and Aviral Kumar. 2025 · 2025
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Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold
Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, and Aviral Kumar. 2025 · 2025
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Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory Wornell, Subhro Das, David Cox, and Chuang Gan. 2025 · 2025
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Kimi k1. 5: Scaling reinforcement learning with llms
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. 2025 · 2025
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7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient
Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 2025 · 2025
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