John Schulman

According to our database, John Schulman authored at least 54 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Measuring short-form factuality in large language models.

Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, William Fedus

CoRR, 2024

Rule Based Rewards for Language Model Safety.

Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, Lilian Weng

CoRR, 2024

Let's Verify Step by Step.

Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Scaling laws for single-agent reinforcement learning.

Jacob Hilton, Jie Tang, John Schulman

CoRR, 2023

Scaling Laws for Reward Model Overoptimization.

Leo Gao, John Schulman, Jacob Hilton

Proceedings of the International Conference on Machine Learning, 2023

2022

Efficient Training of Language Models to Fill in the Middle.

Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen

CoRR, 2022

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, Ryan Lowe

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Batch size-invariance for policy optimization.

Jacob Hilton, Karl Cobbe, John Schulman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

WebGPT: Browser-assisted question-answering with human feedback.

Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman

CoRR, 2021

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, John Schulman

CoRR, 2021

Unsolved Problems in ML Safety.

Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt

CoRR, 2021

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors.

William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada P. Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

CoRR, 2021

Phasic Policy Gradient.

Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Teacher-Student Curriculum Learning.

Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman

IEEE Trans. Neural Networks Learn. Syst., 2020

Scaling Laws for Autoregressive Generative Modeling.

Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish

CoRR, 2020

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark.

Sharada P. Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Grazvydas Semetulskis, João Schapke, Jonas Kubilius, Jurgis Pasukonis, Linas Klimas, Matthew J. Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

Distribution Augmentation for Generative Modeling.

Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever

Proceedings of the 37th International Conference on Machine Learning, 2020

Leveraging Procedural Generation to Benchmark Reinforcement Learning.

Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees.

Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman

CoRR, 2019

Semi-Supervised Learning by Label Gradient Alignment.

Jacob Jackson, John Schulman

CoRR, 2019

Quantifying Generalization in Reinforcement Learning.

Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Gotta Learn Fast: A New Benchmark for Generalization in RL.

Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman

CoRR, 2018

On First-Order Meta-Learning Algorithms.

Alex Nichol, Joshua Achiam, John Schulman

CoRR, 2018

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine

Proceedings of the Robotics: Science and Systems XIV, 2018

Meta Learning Shared Hierarchies.

Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman

Proceedings of the 6th International Conference on Learning Representations, 2018

Model-Based Reinforcement Learning via Meta-Policy Optimization.

Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine

CoRR, 2017

Proximal Policy Optimization Algorithms.

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov

CoRR, 2017

Equivalence Between Policy Gradients and Soft Q-Learning.

John Schulman, Pieter Abbeel, Xi Chen

CoRR, 2017

UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles.

Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman

CoRR, 2017

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning.

Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Lossy Autoencoder.

Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs.

John Schulman

PhD thesis, 2016

High-Dimensional Continuous Control Using Generalized Advantage Estimation.

John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel

Proceedings of the 4th International Conference on Learning Representations, 2016

Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks.

Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

CoRR, 2016

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel

CoRR, 2016

OpenAI Gym.

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba

CoRR, 2016

Concrete Problems in AI Safety.

Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, Dan Mané

CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.

Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

CoRR, 2016

VIME: Variational Information Maximizing Exploration.

Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Benchmarking Deep Reinforcement Learning for Continuous Control.

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Gradient Estimation Using Stochastic Computation Graphs.

John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Trust Region Policy Optimization.

John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Motion planning with sequential convex optimization and convex collision checking.

John Schulman, Yan Duan, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel

Int. J. Robotics Res., 2014

Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation.

Sachin Patil, Gregory Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel

Proceedings of the Algorithmic Foundations of Robotics XI, 2014

Gaussian belief space planning with discontinuities in sensing domains.

Sachin Patil, Yan Duan, John Schulman, Ken Goldberg, Pieter Abbeel

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

Planning locally optimal, curvature-constrained trajectories in 3D using sequential convex optimization.

Yan Duan, Sachin Patil, John Schulman, Kenneth Y. Goldberg, Pieter Abbeel

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

2013

Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization.

John Schulman, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Pieter Abbeel

Proceedings of the Robotics: Science and Systems IX, Technische Universität Berlin, Berlin, Germany, June 24, 2013

Learning from Demonstrations Through the Use of Non-rigid Registration.

John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel

Proceedings of the Robotics Research, 2013

A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario.

John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel

Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles.

Alex X. Lee, Yan Duan, Sachin Patil, John Schulman, Zoe McCarthy, Jur van den Berg, Ken Goldberg, Pieter Abbeel

Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

Tracking deformable objects with point clouds.

John Schulman, Alex X. Lee, Jonathan Ho, Pieter Abbeel

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

2011

Grasping and Fixturing as Submodular Coverage Problems.

John D. Schulman, Ken Goldberg, Pieter Abbeel

Proceedings of the Robotics Research, 2011