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Reinforcement Learning (RL) has become the most effective post-training approach for improving the capabilities of Large Language Models (LLMs).
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) · 1901
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M. M. A., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B. (2019) · 1909
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al. (2019) · 1912
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937
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Eligibility traces for off-policy policy evaluation
Precup, D., Sutton, R. S., and Singh, S. (2000) · 2000
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., and et al. (2020) · 2001
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Approximately optimal approximate reinforcement learning
Kakade, S. and Langford, J. (2002) · 2002
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On a connection between importance sampling and the likelihood ratio policy gradient
Jie, T. and Abbeel, P. (2010) · 2010
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GPU Direct RDMA for infiniband on NVIDIA GPUs: A case study
Potluri, S. and et al. (2013) · 2013
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Project Adam: Building an efficient and scalable deep learning training system
Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., and Long, J. (2014) · 2014
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
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Safe and efficient off-policy reinforcement learning
Munos, R., Stepleton, T., Harutyunyan, A., and Bellemare, M. (2016) · 2016
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Sample efficient actor-critic with experience replay
Wang, Z., Bapst, V., Heess, N., Mnih, V., Munos, R., Kavukcuoglu, K., and De Freitas, N. (2016) · 2016
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Deep reinforcement learning from human preferences
Christiano, P., Leike, J., Brown, T., and et al. (2017) · 2017
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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) · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2017) · 2017
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Sympy: symbolic computing in python
Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., Rathnayake, T., Vig, S., Granger, B. E., Muller, R. P., Bonazzi, F., Gupta, H., Vats, S., Johansson, F., Pedregosa, F., Curry, M. J., Terrel, A. R., Roučka, v., Saboo, A., Fernando, I., Kulal, S., Cimrman, R., and Scopatz, A. (2017) · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017) · 2017
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Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019) · 2017
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., et al. (2018) · 2018
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Recurrent experience replay in distributed reinforcement learning
Kapturowski, S., Ostrovski, G., Quan, J., Munos, R., and Dabney, W. (2018) · 2018
Cited alongside, same era.
Ray: A Distributed Framework for Emerging AI Applications
Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., and Stoica, I. (2018) · 2018
Cited alongside, same era.
Elf opengo: An analysis and open reimplementation of alphazero
Tian, Y., Ma, J., Gong, Q., Sengupta, S., Chen, Z., Pinkerton, J., and Zitnick, L. (2019) · 2019
Cited alongside, same era.
Alphastar: Mastering the real-time strategy game starcraft ii
Vinyals, O., Babuschkin, I., Chung, J., Mathieu, M., Jaderberg, M., Czarnecki, W. M., Dudzik, A., Huang, A., Georgiev, P., Powell, R., et al. (2019) · 2019
Cited alongside, same era.
Zero: Memory optimization towards training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y. (2020) · 2020
Cited alongside, same era.
Pytorch fsdp: Experiences on scaling fully sharded data parallel
Zhao, Y., Gu, A., Varma, R., Luo, L., Huang, C.-C., Xu, M., Wright, L., Shojanazeri, H., Ott, M., Shleifer, S., Desmaison, A., Balioglu, C., Damania, P., Nguyen, B., Chauhan, G., Hao, Y., Mathews, A., and Li, S. (2023) · 2023
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Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms
Ahmadian, A., Cremer, C., Gallé, M., Fadaee, M., Kreutzer, J., Pietquin, O., Üstün, A., and Hooker, S. (2024) · 2024
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The llama 3 herd of models
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Stiennon, N., Ouyang, L., Wu, J., and et al. (2020) · 2020
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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) · 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. X., and Steinhardt, J. (2021) · 2021
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Accelerating PyTorch with CUDA graphs
Nguyen, V., Carilli, M., Eryilmaz, S. B., Singh, V., Lin, M., Gimelshein, N., Desmaison, A., and Yang, E. (2021) · 2021
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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) · 2022
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Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., and et al. (2022) · 2022
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Rlef: Grounding code llms in execution feedback with reinforcement learning
Gehring, J., Zheng, K., Copet, J., Mella, V., Cohen, T., and Synnaeve, G. (2024) · 2024
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Deliberative alignment: Reasoning enables safer language models
Guan, M. Y., Joglekar, M., Wallace, E., Jain, S., Barak, B., Helyar, A., Dias, R., Vallone, A., Ren, H., Wei, J., et al. (2024) · 2024
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Openrlhf: An easy-to-use, scalable and high-performance rlhf framework
Hu, J., Wu, X., Wang, W., Xianyu, Zhang, D., and Cao, Y. (2024) · 2024
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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
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Vineppo: Unlocking rl potential for llm reasoning through refined credit assignment
Kazemnejad, A., Aghajohari, M., Portelance, E., Sordoni, A., Reddy, S., Courville, A., and Roux, N. L. (2024) · 2024
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Tülu 3: Pushing frontiers in open language model post-training
Lambert, N., Morrison, J. D., Pyatkin, V., Huang, S., Ivison, H., Brahman, F., Miranda, L. J. V., Liu, A., Dziri, N., Lyu, X., Gu, Y., Malik, S., Graf, V., Hwang, J. D., Yang, J., Bras, R. L., Tafjord, O., Wilhelm, C., Soldaini, L., Smith, N. A., Wang, Y., Dasigi, P., and Hajishirzi, H. (2024) · 2024
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Remax: A simple, effective, and efficient reinforcement learning method for aligning large language models
Li, Z., Xu, T., Zhang, Y., Lin, Z., Yu, Y., Sun, R., and Luo, Z.-Q. (2024) · 2024
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Llama Team (2024) · 2024
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Asynchronous rlhf: Faster and more efficient off-policy rl for language models
Noukhovitch, M., Huang, S., Xhonneux, S., Hosseini, A., Agarwal, R., and Courville, A. (2024) · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y., Wu, Y., et al. (2024) · 2024
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Nemo-aligner: Scalable toolkit for efficient model alignment
Shen, G., Wang, Z., Delalleau, O., Zeng, J., Dong, Y., Egert, D., Sun, S., Zhang, J., Jain, S., Taghibakhshi, A., Ausin, M. S., Aithal, A., and Kuchaiev, O. (2024) · 2024
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Hybridflow: A flexible and efficient rlhf framework
Sheng, G., Zhang, C., Ye, Z., Wu, X., Zhang, W., Zhang, R., Peng, Y., Lin, H., and Wu, C. (2024) · 2024
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The perfect blend: Redefining rlhf with mixture of judges
Xu, T., Helenowski, E., Sankararaman, K. A., Jin, D., Peng, K., Han, E., Nie, S., Zhu, C., Zhang, H., Zhou, W., Zeng, Z., He, Y., Mandyam, K., Talabzadeh, A., Khabsa, M., Cohen, G., Tian, Y., Ma, H., Wang, S., and Fang, H. (2024) · 2024
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When scaling meets llm finetuning: The effect of data, model and finetuning method
Zhang, B., Liu, Z., Cherry, C., and Firat, O. (2024) · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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