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In this work, we propose Reinforced Functional Token Tuning (RFTT), a novel reinforced fine-tuning framework that empowers Large Language Models (LLMs) with self-play learn-to-reason capabilities.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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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
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Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V. V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., Wu, Y., Neyshabur, B., Gur-Ari, G., and Misra, V · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N. D · 2022
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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.
Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z · 2023
Earlier work this paper cites.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., and Stoica, I · 2023
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Coannotating: Uncertainty-guided work allocation between human and large language models for data annotation
Li, M., Shi, T., Ziems, C., Kan, M., Chen, N. F., Liu, Z., and Yang, D · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2023
Earlier work this paper cites.
Scaling relationship on learning mathematical reasoning with large language models
Yuan, Z., Yuan, H., Li, C., Dong, G., Lu, K., Tan, C., Zhou, C., and Zhou, J · 2023
Earlier work this paper cites.
Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2023
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q. V., and Chi, E. H · 2023
Cited alongside, same era.
Abdin, M., Aneja, J., Behl, H., Bubeck, S., Eldan, R., Gunasekar, S., Harrison, M., Hewett, R. J., Javaheripi, M., Kauffmann, P., et al · 2024
Cited alongside, same era.
Large language models for mathematical reasoning: Progresses and challenges
Ahn, J., Verma, R., Lou, R., Liu, D., Zhang, R., and Yin, W · 2024
Cited alongside, same era.
Claude 3.5 sonnet model card addendum
Anthropic, A · 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
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Imitate, explore, and self-improve: A reproduction report on slow-thinking reasoning systems
Min, Y., Chen, Z., Jiang, J., Chen, J., Deng, J., Hu, Y., Tang, Y., Wang, J., Cheng, X., Song, H., et al · 2024
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Mutual reasoning makes smaller llms stronger problem-solvers
Qi, Z., Ma, M., Xu, J., Zhang, L. L., Yang, F., and Yang, M · 2024
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Algorithm of thoughts: Enhancing exploration of ideas in large language models
Sel, B., Al-Tawaha, A., Khattar, V., Jia, R., and Jin, M · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y., Wu, Y., et al · 2024
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Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., and Mirhoseini, A · 2024
Cited alongside, same era.
Alphamath almost zero: Process supervision without process
Chen, G., Liao, M., Li, C., and Fan, K · 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.
Olympiadbench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems
He, C., Luo, R., Bai, Y., Hu, S., Thai, Z. L., Shen, J., Hu, J., Han, X., Huang, Y., Zhang, Y., Liu, J., Qi, L., Liu, Z., and Sun, M · 2024
Cited alongside, same era.
Openrlhf: An easy-to-use, scalable and high-performance rlhf framework
Hu, J., Wu, X., Wang, W., Zhang, D., Cao, Y., et al · 2024
Cited alongside, same era.
Huang, Z., Zou, H., Li, X., Liu, Y., Zheng, Y., Chern, E., Xia, S., Qin, Y., Yuan, W., and Liu, P · 2024
Cited alongside, same era.
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Toward self-improvement of llms via imagination, searching, and criticizing
Tian, Y., Peng, B., Song, L., Jin, L., Yu, D., Han, L., Mi, H., and Yu, D · 2024
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Reft: Reasoning with reinforced fine-tuning
Trung, L. Q., Zhang, X., Jie, Z., Sun, P., Jin, X., and Li, H · 2024
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Self-play preference optimization for language model alignment
Wu, Y., Sun, Z., Yuan, H., Ji, K., Yang, Y., and Gu, Q · 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., and Ma, Y · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Zhuo, T. Y., Vu, M. C., Chim, J., Hu, H., Yu, W., Widyasari, R., Yusuf, I. N. B., Zhan, H., He, J., Paul, I., et al · 2024
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rstar-math: Small llms can master math reasoning with self-evolved deep thinking
Guan, X., Zhang, L. L., Liu, Y., Shang, N., Sun, Y., Zhu, Y., Yang, F., and Yang, M · 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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Reinforce++: A simple and efficient approach for aligning large language models
Hu, J · 2025
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Advancing reasoning in large language models: Promising methods and approaches
Patil, A · 2025
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Kimi k1. 5: Scaling reinforcement learning with llms
Team, K., Du, A., Gao, B., Xing, B., Jiang, C., Chen, C., Li, C., Xiao, C., Du, C., Liao, C., et al · 2025
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Towards large reasoning models: A survey of reinforced reasoning with large language models
Xu, F., Hao, Q., Zong, Z., Wang, J., Zhang, Y., Wang, J., Lan, X., Gong, J., Ouyang, T., Meng, F., et al · 2025
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