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Process-level Reward Models (PRMs) are crucial for complex reasoning and decision-making tasks, where each intermediate step plays an important role in the reasoning process.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Earlier work this paper cites.
Roscoe: A suite of metrics for scoring step-by-step reasoning
Olga Golovneva, Moya Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz. 2022 · 2022
Earlier work this paper cites.
Solving math word problems with process-and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022
Earlier work this paper cites.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023
Earlier work this paper cites.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023 · 2023
Earlier work this paper cites.
Math-shepherd: Verify and reinforce llms step-by-step without human annotations
Peiyi Wang, Lei Li, Zhihong Shao, RX Xu, Damai Dai, Yifei Li, Deli Chen, Y Wu, and Zhifang Sui. 2023 · 2023
Earlier work this paper cites.
Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023 · 2023
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Mr-gsm8k: A meta-reasoning benchmark for large language model evaluation
Zhongshen Zeng, Pengguang Chen, Shu Liu, Haiyun Jiang, and Jiaya Jia. 2023 · 2023
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Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023 · 2023
Earlier work this paper cites.
OpenScholar: Synthesizing scientific literature with retrieval-augmented language models
Akari Asai, Jacqueline He*, Rulin Shao*, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Tian, D’arcy Mike, David Wadden, Matt Latzke, Minyang, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Dan Weld, Graham Neubig, Doug Downey, Wen-tau Yih, Pang Wei Koh, and Hannaneh Hajishirzi. 2024 · 2024
Cited alongside, same era.
Gemini 2.0 flash experimental
DeepMind. 2024 · 2024
Cited alongside, same era.
Llm critics help catch bugs in mathematics: Towards a better mathematical verifier with natural language feedback
Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Junyang Lin, Chang Zhou, Wen Xiao, et al. 2024 · 2024
Cited alongside, same era.
Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al. 2024 · 2024
Cited alongside, same era.
Criticbench: Benchmarking llms for critique-correct reasoning
Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, Yi-Fan Zhang, Tianlong Xu, Zhendong Chu, et al. 2024 · 2024
Later among the works it cites.
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
Later among the works it cites.
Is your model really a good math reasoner? evaluating mathematical reasoning with checklist
Zihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu, Jindong Wang, Derek F Wong, Xiaowei Huang, Qiufeng Wang, and Kaizhu Huang. 2024 · 2024
Later among the works it cites.
Pure: Prm is still effective and compute-efficient for llm math reasoning
Jie Cheng, Lijun Li, Gang Xiong, Jing Shao, and Yisheng Lv. 2025 · 2025
Closest in time.
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Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo, Haowei Liu, and Yujiu Yang. 2024 · 2024
Cited alongside, same era.
Rm-bench: Benchmarking reward models of language models with subtlety and style
Yantao Liu, Zijun Yao, Rui Min, Yixin Cao, Lei Hou, and Juanzi Li. 2024 · 2024
Cited alongside, same era.
Llm critics help catch llm bugs
Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, and Jan Leike. 2024 · 2024
Cited alongside, same era.
Skywork-o1 open series
Skywork o1 Team. 2024 · 2024
Cited alongside, same era.
Gpt-4o system card
OpenAI. 2024a · 2024
Cited alongside, same era.
O1 replication journey: A strategic progress report–part 1
Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, et al. 2024 · 2024
Cited alongside, same era.
An implementation of generative prm
Wei Xiong, Hanning Zhang, Nan Jiang, and Tong Zhang. 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. 2024a
Cited in the paper.
On designing effective RL reward at training time for LLM reasoning
Jiaxuan Gao, Shusheng Xu, Wenjie Ye, Weilin Liu, Chuyi He, Wei Fu, Zhiyu Mei, Guangju Wang, and Yi Wu. 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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Can mllms reason in multimodality? emma: An enhanced multimodal reasoning benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li, Zhengyuan Yang, Lijuan Wang, and Yu Cheng. 2025 · 2025
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A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond
Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, et al. 2025 · 2025
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Openthinkimg: Learning to think with images via visual tool reinforcement learning
Zhaochen Su, Linjie Li, Mingyang Song, Yunzhuo Hao, Zhengyuan Yang, Jun Zhang, Guanjie Chen, Jiawei Gu, Juntao Li, Xiaoye Qu, et al. 2025 · 2025
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Rethinking reward model evaluation: Are we barking up the wrong tree?
Xueru Wen, Jie Lou, Yaojie Lu, Hongyu Lin, XingYu, Xinyu Lu, Ben He, Xianpei Han, Debing Zhang, and Le Sun. 2025 · 2025
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The lessons of developing process reward models in mathematical reasoning
Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025 · 2025
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