CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model

Ling, W., Yogatama, D., Dyer, C., Blunsom, P.: Program induction by rationale generation: Learning to solve and explain algebraic word problems. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 158–167 (2017)

2017

Earlier work this paper cites.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

2020

Earlier work this paper cites.

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.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)

Original

2021

Earlier work this paper cites.

Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al.: Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332 (2021)

Original

2021

Earlier work this paper cites.

Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al.: Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168 (2021)

Original

2021

Earlier work this paper cites.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al

2022

Earlier work this paper cites.

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al

2022

Earlier work this paper cites.

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems 36

2023

Earlier work this paper cites.

Hao, S., Gu, Y., Ma, H., Hong, J., Wang, Z., Wang, D., Hu, Z.: Reasoning with language model is planning with world model. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (2023). Association for Computational Linguistics

2023

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.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Original

2023

Earlier work this paper cites.

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

Original

2023

Earlier work this paper cites.

Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Alpaca: A strong, replicable instruction-following model. Stanford Center for Research on Foundation Models. https://crfm. stanford. edu/2023/03/13/alpaca. html 3

2023

Earlier work this paper cites.

Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Derczynski, L., et al.: Rwkv: Reinventing rnns for the transformer era. Findings of the Association for Computational Linguistics: EMNLP 2023 (2023)

2023

Earlier work this paper cites.

Li, Z., Yang, Z., Wang, M.: Reinforcement learning with human feedback: Learning dynamic choices via pessimism. arXiv preprint arXiv:2305.18438 (2023)

Original

2023

Earlier work this paper cites.

Jiang, Z., Xu, F.F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., Neubig, G.: Active retrieval augmented generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7969–7992 (2023)

2023

Earlier work this paper cites.

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: React: Synergizing reasoning and acting in language models. In: International Conference on Learning Representations (ICLR) (2023)

2023

Earlier work this paper cites.

Kim, G., Baldi, P., McAleer, S.: Language models can solve computer tasks. Advances in Neural Information Processing Systems 36

2023

Earlier work this paper cites.

Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., Cobbe, K.: Let’s verify step by step. arXiv preprint arXiv:2305.20050 (2023)

Original

2023

Earlier work this paper cites.

Zhong, T., Liu, Z., Pan, Y., Zhang, Y., Zhou, Y., Liang, S., Wu, Z., Lyu, Y., Shu, P., Yu, X., et al.: Evaluation of openai o1: Opportunities and challenges of agi. arXiv preprint arXiv:2409.18486 (2024)

Original

2024

Earlier work this paper cites.

Team, Q.: Qwq: Reflect deeply on the boundaries of the unknown. Hugging Face (2024)

2024

Earlier work this paper cites.

Sun, L., Liang, H., Wei, J., Yu, B., He, C., Zhou, Z., Zhang, W.: Beats: Optimizing llm mathematical capabilities with backverify and adaptive disambiguate based efficient tree search. arXiv preprint arXiv:2409.17972 (2024)

Original

2024

Earlier work this paper cites.

Wan, Z., Feng, X., Wen, M., McAleer, S.M., Wen, Y., Zhang, W., Wang, J.: Alphazero-like tree-search can guide large language model decoding and training. In: Forty-first International Conference on Machine Learning (2024)

2024

Earlier work this paper cites.

Qi, Z., Ma, M., Xu, J., Zhang, L.L., Yang, F., Yang, M.: Mutual reasoning makes smaller llms stronger problem-solvers. arXiv preprint arXiv:2408.06195 (2024)

Original

2024

Earlier work this paper cites.

Zhang, D., Zhoubian, S., Hu, Z., Yue, Y., Dong, Y., Tang, J.: Rest-mcts*: Llm self-training via process reward guided tree search. Advances in Neural Information Processing Systems 37

2024

Cited alongside, same era.

Luo, L., Liu, Y., Liu, R., Phatale, S., Guo, M., Lara, H., Li, Y., Shu, L., Zhu, Y., Meng, L., et al.: Improve mathematical reasoning in language models by automated process supervision. arXiv preprint arXiv:2406.06592 (2024)

Original

2024

Cited alongside, same era.

Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q.V., Ré, C., Mirhoseini, A.: Large language monkeys: Scaling inference compute with repeated sampling. arXiv preprint arXiv:2407.21787 (2024)

Original

2024

Cited alongside, same era.

Li, C., Wang, W., Hu, J., Wei, Y., Zheng, N., Hu, H., Zhang, Z., Peng, H.: Common 7b language models already possess strong math capabilities. arXiv preprint arXiv:2403.04706 (2024)

Original

2024

Cited alongside, same era.

Feng, X., Wan, Z., Fu, H., Liu, B., Yang, M., Koushik, G.A., Hu, Z., Wen, Y., Wang, J.: Natural language reinforcement learning. arXiv preprint arXiv:2411.14251 (2024)

Original

2024

Later among the works it cites.

Schwarzschild, A., Feng, Z., Maini, P., Lipton, Z., Kolter, J.Z.: Rethinking llm memorization through the lens of adversarial compression. Advances in Neural Information Processing Systems 37

2024

Later among the works it cites.

Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., Saldyt, L.P., Murthy, A.B.: Position: Llms can’t plan, but can help planning in llm-modulo frameworks. In: Forty-first International Conference on Machine Learning (2024)

2024

Later among the works it cites.

Zelikman, E., Lorch, E., Mackey, L., Kalai, A.T.: Self-taught optimizer (stop): Recursively self-improving code generation. In: First Conference on Language Modeling (2024)

2024

Later among the works it cites.