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The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science.
Centroidal Voronoi Tessellations: Applications and Algorithms
Qiang Du, Vance Faber, and Max Gunzburger · 1999
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Creating informatics olympiad tasks: exploring the black art
Benjamin A Burton and Mathias Hiron · 2008
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Cédric Colas, Tristan Karch, Olivier Sigaud, and Pierre-Yves Oudeyer · 2012
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2012
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Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem
Jürgen Schmidhuber · 2013
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Self-organization of early vocal development in infants and machines: the role of intrinsic motivation
Clément Moulin-Frier, Sao M Nguyen, and Pierre-Yves Oudeyer · 2014
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
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Quality Diversity: A New Frontier for Evolutionary Computation
Justin Pugh, Lisa Soros, and Kenneth Stanley · 2016
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How evolution may work through curiosity-driven developmental process
Pierre-Yves Oudeyer and Linda B Smith · 2016
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Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris · 2017
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Vassilis Vassiliades, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret · 2017
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
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Great circle of mysteries
Misha Gromov · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Curiosity driven exploration of learned disentangled goal spaces
Adrien Laversanne-Finot, Alexandre Pere, and Pierre-Yves Oudeyer · 2018
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Intrinsically motivated discovery of diverse patterns in self-organizing systems
Chris Reinke, Mayalen Etcheverry, and Pierre-Yves Oudeyer · 2019
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Play, curiosity, and cognition
Junyi Chu and Laura E. Schulz · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Automatic curriculum learning for deep rl: A short survey
Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, and Pierre-Yves Oudeyer · 2020
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A curious formulation robot enables the discovery of a novel protocell behavior
Jonathan Grizou, Laurie J. Points, Abhishek Sharma, and Leroy Cronin · 2020
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Hierarchically organized latent modules for exploratory search in morphogenetic systems
Mayalen Etcheverry, Clément Moulin-Frier, and Pierre-Yves Oudeyer · 2020
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Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems
Chris Reinke, Mayalen Etcheverry, and Pierre-Yves Oudeyer · 2020
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Evaluating Large Language Models Trained on Code, July 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde 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, Josh 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
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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OMNI: Open-endedness via Models of human Notions of Interestingness, June 2023
Jenny Zhang, Joel Lehman, Kenneth Stanley, and Jeff Clune · 2023
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Motif: Intrinsic motivation from artificial intelligence feedback
Martin Klissarov, Pierluca D’Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, and Mikael Henaff · 2023
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Augmenting Autotelic Agents with Large Language Models
Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, and Marc-Alexandre Côté · 2023
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Programming Puzzles
Tal Schuster, Ashwin Kalyan, Alex Polozov, and Adam Tauman Kalai · 2021
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Learning one abstract bit at a time through self-invented experiments encoded as neural networks
Vincent Herrmann, Louis Kirsch, and Jürgen Schmidhuber · 2022
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Language Models Can Teach Themselves to Program Better
Patrick Haluptzok, Matthew Bowers, and Adam Tauman Kalai · 2022
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Evolution through Large Models, June 2022
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O. Stanley · 2022
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Intrinsically motivated goal exploration processes with automatic curriculum learning
Sébastien Forestier, Rémy Portelas, Yoan Mollard, and Pierre-Yves Oudeyer · 2022
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Constitutional AI: Harmlessness from AI Feedback, December 2022
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan · 2022
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Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, and Jacob Andreas · 2023
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Code llama: Open foundation models for code
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
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Tinystories: How small can language models be and still speak coherent english?
Ronen Eldan and Yuanzhi Li · 2023
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Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang · 2023
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Wizardcoder: Empowering code large language models with evol-instruct
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Skill-mix: A flexible and expandable family of evaluations for ai models
Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, and Sanjeev Arora · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
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In praise of folly: flexible goals and human cognition
Junyi Chu, Joshua B. Tenenbaum, and Laura E. Schulz · 2024
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Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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How to train data-efficient llms
Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H Chi, James Caverlee, Julian McAuley, and Derek Zhiyuan Cheng · 2024
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Rainbow teaming: Open-ended generation of diverse adversarial prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, et al · 2024
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Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
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