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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Minerl: A large-scale dataset of minecraft demonstrations
William H Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Uncertainty-based offline reinforcement learning with diversified q-ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Umbrella: Uncertainty-aware model-based offline reinforcement learning leveraging planning
Christopher P. Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, and Torsten Bertram · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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A workflow for offline model-free robotic reinforcement learning
Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, and Sergey Levine · 2021
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Let offline rl flow: Training conservative agents in the latent space of normalizing flows
Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, and Sergey Kolesnikov · 2022
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Off-policy actor-critic for recommender systems
Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, and Ed H. Chi · 2022
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Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters
Kamyar Ghasemipour, Shixiang Shane Gu, and Ofir Nachum · 2022
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Reinforced self-training (rest) for language modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al · 2023
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander J. Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Small language models fine-tuned to coordinate larger language models improve complex reasoning
Gurusha Juneja, Subhabrata Dutta, Soumen Chakrabarti, Sunny Manchanda, and Tanmoy Chakraborty · 2023
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Automatic generation of socratic subquestions for teaching math word problems
Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, and Mrinmaya Sachan · 2022
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A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning, August 2022
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
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Offline rl for natural language generation with implicit language q learning
Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, and Sergey Levine · 2022
Cited alongside, same era.
Corl: Research-oriented deep offline reinforcement learning library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov · 2022
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Katakomba: Tools and benchmarks for data-driven nethack
Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, and Sergey Kolesnikov · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi · 2023
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Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Teaching small language models to reason, 2023
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn · 2023
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Anti-exploration by random network distillation
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, and Sergey Kolesnikov · 2023
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Gpt-4 technical report
R OpenAI · 2023
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Distilling reasoning capabilities into smaller language models
Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan · 2023
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Offline rl for generative design of protein binders
Denis Tarasov, Ulrich Armel Mbou Sob, Miguel Arbesu, Nima Siboni, Sebastien Boyer, Marcin Skwark, Andries Smit, Oliver Bent, and Arnu Pretorius · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al · 2023
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Scaling relationship on learning mathematical reasoning with large language models
Zheng Yuan, Hongyi Yuan, Cheng Li, Guanting Dong, Chuanqi Tan, and Chang Zhou · 2023
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