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We introduce Reward-Guided Speculative Decoding (RSD), a novel framework aimed at improving the efficiency of inference in large language models (LLMs).
Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M. M. A., Yang, Y., and Zhou, Y · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Blockwise parallel decoding for deep autoregressive models
Stern, M., Shazeer, N., and Uszkoreit, J · 2018
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Language models are few-shot learners
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.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Earlier work this paper cites.
The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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
Earlier work this paper cites.
Carbon emissions and large neural network training
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., and Dean, J · 2021
Earlier work this paper cites.
Gptq: Accurate post-training quantization for generative pre-trained transformers
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2022
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
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Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V. V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., Wu, Y., Neyshabur, B., Gur-Ari, G., and Misra, V · 2022
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Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Zhang, Y., Chow, W., Pan, R., Diao, S., Zhang, J., Shum, K., and Zhang, T · 2023
Earlier work this paper cites.
Alpacafarm: A simulation framework for methods that learn from human feedback, 2023
Dubois, Y., Li, X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
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Alpacaeval: An automatic evaluator of instruction-following models
Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Earlier work this paper cites.
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
Earlier work this paper cites.
Gpqa: A graduate-level google-proof q&a benchmark
Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., and Bowman, S. R · 2023
Cited alongside, same era.
Beyond chinchilla-optimal: Accounting for inference in language model scaling laws
Sardana, N., Portes, J., Doubov, S., and Frankle, J · 2023
Cited alongside, same era.
A simple and effective pruning approach for large language models
Sun, M., Liu, Z., Bair, A., and Kolter, J. Z · 2023
Cited alongside, same era.
Qa-lora: Quantization-aware low-rank adaptation of large language models
Xu, Y., Xie, L., Gu, X., Chen, X., Chang, H., Zhang, H., Chen, Z., Zhang, X., and Tian, Q · 2023
Cited alongside, same era.
Outcome-supervised verifiers for planning in mathematical reasoning
Mario: Math reasoning with code interpreter output–a reproducible pipeline
Liao, M., Luo, W., Li, C., Wu, J., and Fan, K · 2024
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Awq: Activation-aware weight quantization for on-device llm compression and acceleration
Lin, J., Tang, J., Tang, H., Yang, S., Chen, W.-M., Wang, W.-C., Xiao, G., Dang, X., Gan, C., and Han, S · 2024
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Skywork-reward: Bag of tricks for reward modeling in llms
Liu, C. Y., Zeng, L., Liu, J., Yan, R., He, J., Wang, C., Yan, S., Liu, Y., and Zhou, Y · 2024
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Specinfer: Accelerating large language model serving with tree-based speculative inference and verification
Miao, X., Oliaro, G., Zhang, Z., Cheng, X., Wang, Z., Zhang, Z., Wong, R. Y. Y., Zhu, A., Yang, L., Shi, X., et al · 2024
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Skywork-o1 open series
o1 Team, S · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yu, F., Gao, A., and Wang, B · 2023
Cited alongside, same era.
Claude 3.5 sonnet model card addendum
Anthropic, A · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., and Mirhoseini, A · 2024
Cited alongside, same era.
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
Cited alongside, same era.
Rlhf workflow: From reward modeling to online rlhf
Dong, H., Xiong, W., Pang, B., Wang, H., Zhao, H., Zhou, Y., Jiang, N., Sahoo, D., Xiong, C., and Zhang, T · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Cited alongside, same era.
Layer skip: Enabling early exit inference and self-speculative decoding
Elhoushi, M., Shrivastava, A., Liskovich, D., Hosmer, B., Wasti, B., Lai, L., Mahmoud, A., Acun, B., Agarwal, S., Roman, A., et al · 2024
Cited alongside, same era.
Break the sequential dependency of llm inference using lookahead decoding
Fu, Y., Bailis, P., Stoica, I., and Zhang, H · 2024
Cited alongside, same era.
Learning to reason with llms, 2024
OpenAI · 2024
Later among the works it cites.
Mutual reasoning makes smaller llms stronger problem-solvers
Qi, Z., Ma, M., Xu, J., Zhang, L. L., Yang, F., and Yang, M · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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Mathscale: Scaling instruction tuning for mathematical reasoning
Tang, Z., Zhang, X., Wang, B., and Wei, F · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Team, G., Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al · 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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Xia, H., Yang, Z., Dong, Q., Wang, P., Li, Y., Ge, T., Liu, T., Li, W., and Sui, Z · 2024
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Think: Thinner key cache by query-driven pruning
Xu, Y., Jie, Z., Dong, H., Wang, L., Lu, X., Zhou, A., Saha, A., Xiong, C., and Sahoo, D · 2024
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What matters for model merging at scale?
Yadav, P., Vu, T., Lai, J., Chronopoulou, A., Faruqui, M., Bansal, M., and Munkhdalai, T · 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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Processbench: Identifying process errors in mathematical reasoning
Zheng, C., Zhang, Z., Zhang, B., Lin, R., Lu, K., Yu, B., Liu, D., Zhou, J., and Lin, J · 2024
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The lessons of developing process reward models in mathematical reasoning
Zhang, Z., Zheng, C., Wu, Y., Zhang, B., Lin, R., Yu, B., Liu, D., Zhou, J., and Lin, J · 2025
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Q#: Provably optimal distributional rl for llm post-training
Zhou, J. P., Wang, K., Chang, J., Gao, Z., Kallus, N., Weinberger, K. Q., Brantley, K., and Sun, W · 2025
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