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Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reasoning.
Learning to predict by the methods of temporal differences
Sutton, R. S · 1988
Earlier work this paper cites.
Efficient selectivity and backup operators in monte-carlo tree search
Coulom, R · 2006
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
Earlier work this paper cites.
On the scalability of parallel uct
Segal, R. B · 2010
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Multi-armed bandits with episode context
Rosin, C. D · 2011
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
Earlier work this paper cites.
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.
Learning and planning in complex action spaces
Hubert, T., Schrittwieser, J., Antonoglou, I., Barekatain, M., Schmitt, S., and Silver, D · 2021
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Machine translation decoding beyond beam search
Leblond, R., Alayrac, J.-B., Sifre, L., Pislar, M., Lespiau, J.-B., Antonoglou, I., Simonyan, K., and Vinyals, O · 2021
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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.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
Earlier work this paper cites.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Creswell, A., Shanahan, M., and Higgins, I · 2022
Earlier work this paper cites.
Maieutic prompting: Logically consistent reasoning with recursive explanations
Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., and Choi, Y · 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.
Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al · 2022
Cited alongside, same era.
Inductive program synthesis through using monte carlo tree search guided by a heuristic-based loss function
Matulewicz, N · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Saparov, A. and He, H · 2022
Cited alongside, same era.
Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
Large language models cannot self-correct reasoning yet
Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D · 2023
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Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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Making ppo even better: Value-guided monte-carlo tree search decoding, 2023
Liu, J., Cohen, A., Pasunuru, R., Choi, Y., Hajishirzi, H., and Celikyilmaz, A · 2023
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Large language model guided tree-of-thought
Long, J · 2023
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Cited alongside, same era.
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
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 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
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., et al · 2022
Cited alongside, same era.
Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models, 2023
Abdulhai, M., White, I., Snell, C., Sun, C., Hong, J., Zhai, Y., Xu, K., and Levine, S · 2023
Cited alongside, same era.
Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Pan, R., Diao, S., Zhang, J., Shum, K., and Zhang, T · 2023
Cited alongside, same era.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Luo, H., Sun, Q., Xu, C., Zhao, P., Lou, J., Tao, C., Geng, X., Lin, Q., Chen, S., and Zhang, D · 2023
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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., et al · 2023
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OpenAI · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Shinn, N., Labash, B., and Gopinath, A · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Stechly, K., Marquez, M., and Kambhampati, S · 2023
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Decomposition enhances reasoning via self-evaluation guided decoding
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M.-Y., He, J., and Xie, Q · 2023
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Xu, H · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Planning with large language models for code generation
Zhang, S., Chen, Z., Shen, Y., Ding, M., Tenenbaum, J. B., and Gan, C · 2023
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Lima: Less is more for alignment
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al · 2023
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Solving math word problems via cooperative reasoning induced language models
Zhu, X., Wang, J., Zhang, L., Zhang, Y., Huang, Y., Gan, R., Zhang, J., and Yang, Y · 2023
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