Training Language Models to Reason Efficiently

Zero: Memory optimizations toward training trillion parameter models, 2020

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Buy 4 REINFORCE samples, get a baseline for free!, 2019

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Measuring mathematical problem solving with the math dataset

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Self-consistency improves chain of thought reasoning in language models

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Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022

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Chain-of-thought prompting elicits reasoning in large language models

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Fast inference from transformers via speculative decoding

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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

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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

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Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024

Original

Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y. K., Wu, Y., and Guo, D · 2024

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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 · 2024

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A survey on efficient inference for large language models, 2024

Original

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.-P., Dong, Y., and Wang, Y · 2024

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L1: Controlling how long a reasoning model thinks with reinforcement learning, 2025

Original

Aggarwal, P. and Welleck, S · 2025

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Reasoning models don’t always say what they think, 2025