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Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs).
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry. 1952 · 1952
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2020
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 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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Defining and characterizing reward gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger. 2022 · 2022
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton. 2023 · 2023
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Rewarding chatbots for real-world engagement with millions of users
Robert Irvine, Douglas Boubert, Vyas Raina, Adian Liusie, Ziyi Zhu, Vineet Mudupalli, Aliaksei Korshuk, Zongyi Liu, Fritz Cremer, Valentin Assassi, et al. 2023 · 2023
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Llm-blender: Ensembling large language models with pairwise ranking and generative fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. 2023 · 2023
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Openassistant conversations-democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri Von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, et al. 2023 · 2023
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Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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Nash learning from human feedback
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, et al. 2023 · 2023
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Verbosity bias in preference labeling by large language models
Keita Saito, Akifumi Wachi, Koki Wataoka, and Youhei Akimoto. 2023 · 2023
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A long way to go: Investigating length correlations in rlhf
Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. 2023 · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al. 2023 · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023 · 2023
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Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023 · 2023
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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 · 2024
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Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, et al. 2024 · 2024
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Compassjudger-1: All-in-one judge model helps model evaluation and evolution
Maosong Cao, Alexander Lam, Haodong Duan, Hongwei Liu, Songyang Zhang, and Kai Chen. 2024 · 2024
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Reward model ensembles help mitigate overoptimization
Thomas Coste, Usman Anwar, Robert Kirk, and David Krueger. 2024 · 2024
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Ultrafeedback: Boosting language models with scaled ai feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, et al. 2024 · 2024
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Skywork critic model series
Tu Shiwen, Zhao Liang, Chris Yuhao Liu, Liang Zeng, and Yang Liu. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024
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A minimaximalist approach to reinforcement learning from human feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, and Alekh Agarwal. 2024 · 2024
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Judgebench: A benchmark for evaluating llm-based judges
Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, and Ion Stoica. 2024 · 2024
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Quantile regression for distributional reward models in rlhf
Nicolai Dorka. 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.
Can large language models understand real-world complex instructions?
Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Jiaqing Liang, and Yanghua Xiao. 2024 · 2024
Cited alongside, same era.
Qwen2. 5-coder technical report
Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al. 2024 · 2024
Cited alongside, same era.
Tool-augmented reward modeling
Lei Li, Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Ningyu Zhang, and Hua Wu. 2024 · 2024
Cited alongside, same era.
Flame: Factuality-aware alignment for large language models
Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, and Xilun Chen. 2024 · 2024
Cited alongside, same era.
Uncertainty-aware reward model: Teaching reward models to know what is unknown
Xingzhou Lou, Dong Yan, Wei Shen, Yuzi Yan, Jian Xie, and Junge Zhang. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang. 2024 · 2024
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Deepseek-prover: Advancing theorem proving in llms through large-scale synthetic data
Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, and Xiaodan Liang. 2024 · 2024
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Self-generated critiques boost reward modeling for language models
Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, et al. 2024 · 2024
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Advancing llm reasoning generalists with preference trees
Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, and Maosong Sun. 2024 · 2024
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Atla selene mini: A general purpose evaluation model
Andrei Alexandru, Antonia Calvi, Henry Broomfield, Jackson Golden, Kyle Dai, Mathias Leys, Maurice Burger, Max Bartolo, Roman Engeler, Sashank Pisupati, Toby Drane, and Young Sun Park. 2025 · 2025
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