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OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.OpenAI has claimed that the main techinique behinds o1 is the reinforcement learining.
The monte carlo method
Nicholas Metropolis and Stanislaw Ulam · 1949
Earlier work this paper cites.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto · 1998
Earlier work this paper cites.
Actor-critic algorithms
Vijay R. Konda and John N. Tsitsiklis · 1999
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart Russell · 1999
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David A. McAllester, Satinder Singh, and Yishay Mansour · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart Russell · 2000
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
Earlier work this paper cites.
Self-paced learning for latent variable models
M. Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
Earlier work this paper cites.
Multi-armed bandits with episode context
Christopher D. Rosin · 2011
Earlier work this paper cites.
A survey of monte carlo tree search methods
Cameron Browne, Edward Jack Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez Liebana, Spyridon Samothrakis, and Simon Colton · 2012
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Earlier work this paper cites.
Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
Earlier work this paper cites.
David Ha and Jürgen Schmidhuber · 2018
Earlier work this paper cites.
Toward problem recognition, explanation and goal formulation
Sravya Kondrakunta, Venkatsampath Raja Gogineni, Matt Molineaux, Hector Munoz-Avila, Martin Oxenham, and Michael T Cox · 2018
Earlier work this paper cites.
Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
Earlier work this paper cites.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark J. F. Gales · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford and Karthik Narasimhan · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman · 2018
Earlier work this paper cites.
Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Earlier work this paper cites.
Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy Liang · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
The bitter lesson, 2019
Richard S. Sutton · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Earlier work this paper cites.
Artificial Intelligence: A Modern Approach (4th Edition)
Stuart Russell and Peter Norvig · 2020
Earlier work this paper cites.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, and David Silver · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Earlier work this paper cites.
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, Christopher Hesse, and John Schulman · 2021
Earlier work this paper cites.
Iq-learn: Inverse soft-q learning for imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon · 2021
Earlier work this paper cites.
Alfworld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew J. Hausknecht · 2021
Earlier work this paper cites.
A survey on curriculum learning
Xin Wang, Yudong Chen, and Wenwu Zhu · 2021
Earlier work this paper cites.
Learning to perform complex tasks through compositional fine-tuning of language models
Victor S. Bursztyn, David Demeter, Doug Downey, and Larry Birnbaum · 2022
Earlier work this paper cites.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Earlier work this paper cites.
Successive prompting for decomposing complex questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner · 2022
Earlier work this paper cites.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
Earlier work this paper cites.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
Earlier work this paper cites.
The 37 implementation details of proximal policy optimization
Shengyi Huang, Rousslan Fernand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Earlier work this paper cites.
Why i’m excited about ai-assisted human feedback, 2022
Jan Leike · 2022
Earlier work this paper cites.
Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra · 2022
Earlier work this paper cites.
Human language understanding & reasoning
Christopher D Manning · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
Earlier work this paper cites.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush · 2022
Earlier work this paper cites.
BLOOM: A 176b-parameter open-access multilingual language model
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Earlier work this paper cites.
Natural language to code translation with execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I. Wang · 2022
Earlier work this paper cites.
Super-naturalinstructions: Generalization via declarative instructions on 1600+ NLP tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, and Xudong Shen · 2022
Earlier work this paper cites.
Emergent analogical reasoning in large language models
Taylor W. Webb, Keith J. Holyoak, and Hongjing Lu · 2022
Earlier work this paper cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le · 2022
Earlier work this paper cites.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
Earlier work this paper cites.
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman · 2022
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Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin, Jena D. Hwang, Xiang Lorraine Li, Hirona Jacqueline Arai, Soumya Sanyal, Keisuke Sakaguchi, Xiang Ren, and Yejin Choi · 2023
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Universal self-consistency for large language model generation
Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, and Denny Zhou · 2023
Earlier work this paper cites.
KCTS: knowledge-constrained tree search decoding with token-level hallucination detection
Sehyun Choi, Tianqing Fang, Zhaowei Wang, and Yangqiu Song · 2023
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Introduction to latent variable energy-based models: A path towards autonomous machine intelligence
Anna Dawid and Yann LeCun · 2023
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Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration
Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, and Tat-Seng Chua · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2023
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Language models can teach themselves to program better
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Redpajama: an open dataset for training large language models, 2024
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Language models learn to mislead humans via RLHF
Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, and Shi Feng · 2024
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From language modeling to instruction following: Understanding the behavior shift in llms after instruction tuning
Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, and Dong Yu · 2024
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Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2024
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Monte carlo tree search boosts reasoning via iterative preference learning
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An implementation of generative prm
Wei Xiong, Hanning Zhang, Nan Jiang, and Tong Zhang · 2024
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Wizardlm: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang · 2024
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Mirror: A multiple-perspective self-reflection method for knowledge-rich reasoning
Hanqi Yan, Qinglin Zhu, Xinyu Wang, Lin Gui, and Yulan He · 2024
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Qwen2.5-math technical report: Toward mathematical expert model via self-improvement
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Large language models as analogical reasoners
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Physics of language models: Part 2.2, how to learn from mistakes on grade-school math problems
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Generation meets verification: Accelerating large language model inference with smart parallel auto-correct decoding
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Reasoning in flux: Enhancing large language models reasoning through uncertainty-aware adaptive guidance
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Aggregation of reasoning: A hierarchical framework for enhancing answer selection in large language models
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Ovm, outcome-supervised value models for planning in mathematical reasoning
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Thought propagation: an analogical approach to complex reasoning with large language models
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Self-rewarding language models
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Memorize step by step: Efficient long-context prefilling with incremental memory and decremental chunk
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Reverse-o1, 2024
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Self-alignment for factuality: Mitigating hallucinations in LLMs via self-evaluation
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Layer by layer: Uncovering where multi-task learning happens in instruction-tuned large language models
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Opencodeinterpreter: Integrating code generation with execution and refinement
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DPO meets PPO: reinforced token optimization for RLHF
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Language agent tree search unifies reasoning, acting, and planning in language models
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The relationship between intelligence and divergent thinking—a meta-analytic update
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