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Tree-search-based reasoning methods have significantly enhanced the reasoning capability of large language models (LLMs) by facilitating the exploration of multiple intermediate reasoning steps, i.e., thoughts.
Speculative computation, parallelism, and functional programming
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Gopinath, R. A · 1998
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Burkardt, J · 2014
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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., Hesse, C., and Schulman, J · 2021
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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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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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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
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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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Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
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Decomposition enhances reasoning via self-evaluation guided decoding
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M., He, J., and Xie, Q · 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., Cao, Y., and Narasimhan, K · 2023
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Alphamath almost zero: process supervision without process
Chen, G., Liao, M., Li, C., and Fan, K · 2024
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Padellm-ner: Parallel decoding in large language models for named entity recognition
Lu, J., Yang, Z., Wang, Y., Liu, X., Namee, B. M., and Huang, C · 2024
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Treebon: Enhancing inference-time alignment with speculative tree-search and best-of-n sampling
Qiu, J., Lu, Y., Zeng, Y., Guo, J., Geng, J., Wang, H., Huang, K., Wu, Y., and Wang, M · 2024
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Qwen2.5: A party of foundation models, September 2024
Team, Q · 2024
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Alphazero-like tree-search can guide large language model decoding and training
Wan, Z., Feng, X., Wen, M., McAleer, S. M., Wen, Y., Zhang, W., and Wang, J · 2024
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Math-shepherd: Verify and reinforce llms step-by-step without human annotations
Wang, P., Li, L., Shao, Z., Xu, R., Dai, D., Li, Y., Chen, D., Wu, Y., and Sui, Z · 2024
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The llama 3 herd of models
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., Goyal, A., Hartshorn, A., Yang, A., Mitra, A., Sravankumar, A., Korenev, A., Hinsvark, A., Rao, A., Zhang, A., Rodriguez, A., Gregerson, A., Spataru, A., Rozière, B., Biron, B., Tang, B., Chern, B., Caucheteux, C., Nayak, C., Bi, C., Marra, C., McConnell, C., Keller, C., Touret, C., Wu, C., Wong, C., Ferrer, C. C., Nikolaidis, C., Allonsius, D., Song, D., Pintz, D., Livshits, D., Esiobu, D., Choudhary, D., Mahajan, D., Garcia-Olano, D., Perino, D., Hupkes, D., Lakomkin, E., AlBadawy, E., Lobanova, E., Dinan, E., Smith, E. M., Radenovic, F., Zhang, F., Synnaeve, G., Lee, G., Anderson, G. L., Nail, G., Mialon, G., Pang, G., Cucurell, G., Nguyen, H., Korevaar, H., Xu, H., Touvron, H., Zarov, I., Ibarra, I. A., Kloumann, I. M., Misra, I., Evtimov, I., Copet, J., Lee, J., Geffert, J., Vranes, J., Park, J., Mahadeokar, J., Shah, J., van der Linde, J., Billock, J., Hong, J., Lee, J., Fu, J., Chi, J., Huang, J., Liu, J., Wang, J., Yu, J., Bitton, J., Spisak, J., Park, J., Rocca, J., Johnstun, J., Saxe, J., Jia, J., Alwala, K. V., Upasani, K., Plawiak, K., Li, K., Heafield, K., Stone, K., and et al · 2024
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Interpretable contrastive monte carlo tree search reasoning
Gao, Z., Niu, B., He, X., Xu, H., Liu, H., Liu, A., Hu, X., and Wen, L · 2024
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Rot: Enhancing large language models with reflection on search trees
Hui, W., Jiang, C., Wang, Y., and Tu, K · 2024
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Technical report: Enhancing LLM reasoning with reward-guided tree search
Jiang, J., Chen, Z., Min, Y., Chen, J., Cheng, X., Wang, J., Tang, Y., Sun, H., Deng, J., Zhao, W. X., Liu, Z., Yan, D., Xie, J., Wang, Z., and Wen, J · 2024
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Mindstar: Enhancing math reasoning in pre-trained llms at inference time
Kang, J., Li, X. Z., Chen, X., Kazemi, A., and Chen, B · 2024
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Cllms: Consistency large language models
Kou, S., Hu, L., He, Z., Deng, Z., and Zhang, H · 2024
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EAGLE: speculative sampling requires rethinking feature uncertainty
Li, Y., Wei, F., Zhang, C., and Zhang, H · 2024
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Scaling inference computation: Compute-optimal inference for problem-solving with language models
Wu, Y., Sun, Z., Li, S., Welleck, S., and Yang, Y · 2024
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Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding
Xia, H., Yang, Z., Dong, Q., Wang, P., Li, Y., Ge, T., Liu, T., Li, W., and Sui, Z · 2024
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Multi-candidate speculative decoding
Yang, S., Huang, S., Dai, X., and Chen, J · 2024
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S3D: A simple and cost-effective self-speculative decoding scheme for low-memory gpus
Zhong, W. and Bharadwaj, M · 2024
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A survey on efficient inference for large language models
Zhou, Z., Ning, X., Hong, K., Fu, T., Xu, J., Li, S., Lou, Y., Wang, L., Yuan, Z., Li, X., Yan, S., Dai, G., Zhang, X., Dong, Y., and Wang, Y · 2024
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Cantelli’s inequality — Wikipedia, the free encyclopedia, 2024
Wikipedia contributors · 2025
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