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Large reasoning language models such as OpenAI-o1 and Deepseek-R1 have recently attracted widespread attention due to their impressive task-solving abilities.
Blockwise parallel decoding for deep autoregressive models
Stern, M., Shazeer, N., and Uszkoreit, J. (2018) · 2018
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., Hesse, C., and Schulman, J. (2021) · 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) · 2021
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022) · 2022
Earlier work this paper cites.
Rest: Retrieval-based speculative decoding
He, Z., Zhong, Z., Cai, T., Lee, J. D., and He, D. (2023) · 2023
Earlier work this paper cites.
Speculative decoding with big little decoder
Kim, S., Mangalam, K., Moon, S., Malik, J., Mahoney, M. W., Gholami, A., and Keutzer, K. (2023) · 2023
Earlier work this paper cites.
Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y. (2023) · 2023
Earlier work this paper cites.
Accelerating llm inference with staged speculative decoding
Spector, B. and Re, C. (2023) · 2023
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2023) · 2023
Earlier work this paper cites.
Speculative decoding: Exploiting speculative execution for accelerating seq2seq generation
Xia, H., Ge, T., Wang, P., Chen, S.-Q., Wei, F., and Sui, Z. (2023) · 2023
Earlier work this paper cites.
Draft & verify: Lossless large language model acceleration via self-speculative decoding
Zhang, J., Wang, J., Li, H., Shou, L., Chen, K., Chen, G., and Mehrotra, S. (2023) · 2023
Earlier work this paper cites.
Medusa: Simple llm inference acceleration framework with multiple decoding heads
Cai, T., Li, Y., Geng, Z., Peng, H., Lee, J. D., Chen, D., and Dao, T. (2024) · 2024
Cited alongside, same era.
Break the chain: Large language models can be shortcut reasoners
Ding, M., Liu, H., Fu, Z., Song, J., Xie, W., and Zhang, Y. (2024) · 2024
Cited alongside, same era.
Training large language models to reason in a continuous latent space
Hao, S., Sukhbaatar, S., Su, D., Li, X., Hu, Z., Weston, J., and Tian, Y. (2024) · 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., Shen, J., Hu, J., Han, X., Huang, Y., Zhang, Y., Liu, J., Qi, L., Liu, Z., and Sun, M. (2024) · 2024
Cited alongside, same era.
Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al. (2024) · 2024
Sketch-of-thought: Efficient llm reasoning with adaptive cognitive-inspired sketching
Aytes, S. A., Baek, J., and Hwang, S. J. (2025) · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., Xue, B., Wang, B., Wu, B., Feng, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., Dai, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., Hao, G., Chen, G., Li, G., Zhang, H., Bao, H., Xu, H., Wang, H., Ding, H., Xin, H., Gao, H., Qu, H., Li, H., Guo, J., Li, J., Wang, J., Chen, J., Yuan, J., Qiu, J., Li, J., Cai, J. L., Ni, J., Liang, J., Chen, J., Dong, K., Hu, K., Gao, K., Guan, K., Huang, K., Yu, K., Wang, L., Zhang, L., Zhao, L., Wang, L., Zhang, L., Xu, L., Xia, L., Zhang, M., Zhang, M., Tang, M., Li, M., Wang, M., Li, M., Tian, N., Huang, P., Zhang, P., Wang, Q., Chen, Q., Du, Q., Ge, R., Zhang, R., Pan, R., Wang, R., Chen, R. J., Jin, R. L., Chen, R., Lu, S., Zhou, S., Chen, S., Ye, S., Wang, S., Yu, S., Zhou, S., Pan, S., Li, S. S., Zhou, S., Wu, S., Ye, S., Yun, T., Pei, T., Sun, T., Wang, T., Zeng, W., Zhao, W., Liu, W., Liang, W., Gao, W., Yu, W., Zhang, W., Xiao, W. L., An, W., Liu, X., Wang, X., Chen, X., Nie, X., Cheng, X., Liu, X., Xie, X., Liu, X., Yang, X., Li, X., Su, X., Lin, X., Li, X. Q., Jin, X., Shen, X., Chen, X., Sun, X., Wang, X., Song, X., Zhou, X., Wang, X., Shan, X., Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Y., Xu, Y., Li, Y., Zhao, Y., Sun, Y., Wang, Y., Yu, Y., Zhang, Y., Shi, Y., Xiong, Y., He, Y., Piao, Y., Wang, Y., Tan, Y., Ma, Y., Liu, Y., Guo, Y., Ou, Y., Wang, Y., Gong, Y., Zou, Y., He, Y., Xiong, Y., Luo, Y., You, Y., Liu, Y., Zhou, Y., Zhu, Y. X., Xu, Y., Huang, Y., Li, Y., Zheng, Y., Zhu, Y., Ma, Y., Tang, Y., Zha, Y., Yan, Y., Ren, Z. Z., Ren, Z., Sha, Z., Fu, Z., Xu, Z., Xie, Z., Zhang, Z., Hao, Z., Ma, Z., Yan, Z., Wu, Z., Gu, Z., Zhu, Z., Liu, Z., Li, Z., Xie, Z., Song, Z., Pan, Z., Huang, Z., Xu, Z., Zhang, Z., and Zhang, Z. (2025) · 2025
Closest in time.
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Cited alongside, same era.
Eagle: Speculative sampling requires rethinking feature uncertainty
Li, Y., Wei, F., Zhang, C., and Zhang, H. (2024) · 2024
Cited alongside, same era.
Mario: Math reasoning with code interpreter output – a reproducible pipeline
Liao, M., Luo, W., Li, C., Wu, J., and Fan, K. (2024) · 2024
Cited alongside, same era.
Can language models learn to skip steps?
Liu, T., Guo, Q., Hu, X., Jiayang, C., Zhang, Y., Qiu, X., and Zhang, Z. (2024) · 2024
Cited alongside, same era.
Llmlingua-2: Data distillation for efficient and faithful task-agnostic prompt compression
Pan, Z., Wu, Q., Jiang, H., Xia, M., Luo, X., Zhang, J., Lin, Q., Rühle, V., Yang, Y., Lin, C.-Y., et al. (2024) · 2024
Cited alongside, same era.
Mathscale: Scaling instruction tuning for mathematical reasoning
Tang, Z., Zhang, X., Wang, B., and Wei, F. (2024) · 2024
Cited alongside, same era.
Distilling system 2 into system 1
Yu, P., Xu, J., Weston, J., and Kulikov, I. (2024) · 2024
Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J. (2023a)
Cited in the paper.
Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning
Hou, B., Zhang, Y., Ji, J., Liu, Y., Qian, K., Andreas, J., and Chang, S. (2025) · 2025
Closest in time.
SKIntern: Internalizing symbolic knowledge for distilling better CoT capabilities into small language models
Liao, H., He, S., Hao, Y., Li, X., Zhang, Y., Zhao, J., and Liu, K. (2025) · 2025
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Quantization hurts reasoning? an empirical study on quantized reasoning models
Liu, R., Sun, Y., Zhang, M., Bai, H., Yu, X., Yu, T., Yuan, C., and Hou, L. (2025) · 2025
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O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning
Luo, H., Shen, L., He, H., Wang, Y., Liu, S., Li, W., Tan, N., Cao, X., and Tao, D. (2025) · 2025
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Thinking slow, fast: Scaling inference compute with distilled reasoners
Paliotta, D., Wang, J., Pagliardini, M., Li, K. Y., Bick, A., Kolter, J. Z., Gu, A., Fleuret, F., and Dao, T. (2025) · 2025
Closest in time.
Reasoning with latent thoughts: On the power of looped transformers
Saunshi, N., Dikkala, N., Li, Z., Kumar, S., and Reddi, S. J. (2025) · 2025
Closest in time.
Tokenskip: Controllable chain-of-thought compression in llms
Xia, H., Li, Y., Leong, C. T., Wang, W., and Li, W. (2025) · 2025
Closest in time.
Lightthinker: Thinking step-by-step compression
Zhang, J., Zhu, Y., Sun, M., Luo, Y., Qiao, S., Du, L., Zheng, D., Chen, H., and Zhang, N. (2025) · 2025
Closest in time.