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Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Learning with bad training data via iterative trimmed loss minimization
Shen, Y. and Sanghavi, S · 2019
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the MATH dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
Earlier work this paper cites.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., de Las Casas, D., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al · 2022
Earlier work this paper cites.
Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
Earlier work this paper cites.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Earlier work this paper cites.
Chatgpt outperforms crowd workers for text-annotation tasks
Gilardi, F., Alizadeh, M., and Kubli, M · 2023
Earlier work this paper cites.
Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J., Wang, Z., Wang, D., and Hu, Z · 2023
Earlier work this paper cites.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Self-Refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., and Clark, P · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2023
Cited alongside, same era.
Large language models are better reasoners with self-verification
Weng, Y., Zhu, M., Xia, F., Li, B., He, S., Liu, S., Sun, B., Liu, K., and Zhao, J · 2023
Cited alongside, same era.
ReAct: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., and Cao, Y · 2023
Cited alongside, same era.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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Easy-to-hard generalization: Scalable alignment beyond human supervision
Sun, Z., Yu, L., Shen, Y., Liu, W., Yang, Y., Welleck, S., and Gan, C · 2024
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Alphazero-like tree-search can guide large language model decoding and training, 2024
Wan, Z., Feng, X., Wen, M., Wen, Y., Zhang, W., and Wang, J · 2024
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Enhancing llm reasoning via critique models with test-time and training-time supervision
Xi, Z., Yang, D., Huang, J., Tang, J., Li, G., Ding, Y., He, W., Hong, B., Do, S., Zhan, W., et al · 2024
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Self-evaluation guided beam search for reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, J. X., Kan, M.-Y., He, J., and Xie, M · 2024
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Improving factuality and reasoning in language models through multiagent debate, 2024
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Cited alongside, same era.
Can large language models follow concept annotation guidelines? a case study on scientific and financial domains
Fonseca, M. and Cohen, S · 2024
Cited alongside, same era.
CRITIC: Large language models can self-correct with tool-interactive critiquing
Gou, Z., Shao, Z., Gong, Y., yelong shen, Yang, Y., Duan, N., and Chen, W · 2024
Cited alongside, same era.
Minillm: Knowledge distillation of large language models
Gu, Y., Dong, L., Wei, F., and Huang, M · 2024
Cited alongside, same era.
Exploring mathematical extrapolation of large language models with synthetic data
Li, H., Ma, Y., Zhang, Y., Ye, C., and Chen, J · 2024
Cited alongside, same era.
Let’s verify step by step
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2024
Cited alongside, same era.
Later among the works it cites.
An implementation of generative PRM
Xiong, W., Zhang, H., Jiang, N., and Zhang, T · 2024
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LLMRefine: Pinpointing and refining large language models via fine-grained actionable feedback
Xu, W., Deutsch, D., Finkelstein, M., Juraska, J., Zhang, B., Liu, Z., Wang, W. Y., Li, L., and Freitag, M · 2024
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
Later among the works it cites.
Processbench: Identifying process errors in mathematical reasoning
Zheng, C., Zhang, Z., Zhang, B., Lin, R., Lu, K., Yu, B., Liu, D., Zhou, J., and Lin, J · 2024
Later among the works it cites.
Self-training: A survey
Amini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, E., and Maximov, Y · 2025
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DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning
Sprague, Z. R., Yin, F., Rodriguez, J. D., Jiang, D., Wadhwa, M., Singhal, P., Zhao, X., Ye, X., Mahowald, K., and Durrett, G · 2025
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