John Schulman - RankMe

75 papers A* 15A 2C 2Misc 1Journal 45Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Chunky Post-Training: Data Driven Failures of Generalization. CoRR Seoirse Murray, Allison Qi, Timothy Qian, John Schulman, Collin Burns, Sara Price
2025	J	`jnl`	Detecting Adversarial Fine-tuning with Auditing Agents. CoRR Sarah Egler, John Schulman, Nicholas Carlini
2025	J	`jnl`	Reasoning Models Don't Always Say What They Think. CoRR Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vladimir Mikulik, Samuel R. Bowman, Jan Leike, Jared Kaplan, Ethan Perez
2025	J	`jnl`	Stress-Testing Model Specs Reveals Character Differences among Language Models. CoRR Jifan Zhang, Henry Sleight, Andi Peng, John Schulman, Esin Durmus
2024	A*	`conf`	Let's Verify Step by Step. ICLR Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
2024	J	`jnl`	Measuring short-form factuality in large language models. CoRR Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, William Fedus
2024	A*	`conf`	Rule Based Rewards for Language Model Safety. NeurIPS Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, Lilian Weng
2024	J	`jnl`	Rule Based Rewards for Language Model Safety. CoRR Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, Lilian Weng
2023	J	`jnl`	Let's Verify Step by Step. CoRR Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
2023	A*	`conf`	Scaling Laws for Reward Model Overoptimization. ICML Leo Gao, John Schulman, Jacob Hilton
2023	J	`jnl`	Scaling laws for single-agent reinforcement learning. CoRR Jacob Hilton, Jie Tang, John Schulman
2022	A*	`conf`	Batch size-invariance for policy optimization. NeurIPS Jacob Hilton, Karl Cobbe, John Schulman
2022	J	`jnl`	Efficient Training of Language Models to Fill in the Middle. CoRR Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
2022	J	`jnl`	Scaling Laws for Reward Model Overoptimization. CoRR Leo Gao, John Schulman, Jacob Hilton
2022	A*	`conf`	Training language models to follow instructions with human feedback. NeurIPS 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
2022	J	`jnl`	Training language models to follow instructions with human feedback. CoRR Long Ouyang, Jeff 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
2021	J	`jnl`	Batch size-invariance for policy optimization. CoRR Jacob Hilton, Karl Cobbe, John Schulman
2021	J	`jnl`	Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. CoRR 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
2021	A*	`conf`	Phasic Policy Gradient. ICML Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
2021	J	`jnl`	The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR 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
2021	J	`jnl`	Training Verifiers to Solve Math Word Problems. CoRR Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman
2021	J	`jnl`	Unsolved Problems in ML Safety. CoRR Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt
2021	J	`jnl`	WebGPT: Browser-assisted question-answering with human feedback. CoRR 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
2020	A*	`conf`	Distribution Augmentation for Generative Modeling. ICML Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
2020	A*	`conf`	Leveraging Procedural Generation to Benchmark Reinforcement Learning. ICML Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
2020	—	`conf`	Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. NeurIPS (Competition and Demos) 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
2020	J	`jnl`	Phasic Policy Gradient. CoRR Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
2020	J	`jnl`	Scaling Laws for Autoregressive Generative Modeling. CoRR 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
2020	J	`jnl`	Teacher-Student Curriculum Learning. IEEE Trans. Neural Networks Learn. Syst. Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
2019	J	`jnl`	Leveraging Procedural Generation to Benchmark Reinforcement Learning. CoRR Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
2019	J	`jnl`	Policy Gradient Search: Online Planning and Expert Iteration without Search Trees. CoRR Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman
2019	A*	`conf`	Quantifying Generalization in Reinforcement Learning. ICML Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman
2019	J	`jnl`	Semi-Supervised Learning by Label Gradient Alignment. CoRR Jacob Jackson, John Schulman
2018	J	`jnl`	Gotta Learn Fast: A New Benchmark for Generalization in RL. CoRR Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman
2018	—	`conf`	Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. Robotics: Science and Systems Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine
2018	—	`conf`	Meta Learning Shared Hierarchies. ICLR (Poster) Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
2018	Misc	`conf`	Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRL Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
2018	J	`jnl`	Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRR Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
2018	J	`jnl`	On First-Order Meta-Learning Algorithms. CoRR Alex Nichol, Joshua Achiam, John Schulman
2018	J	`jnl`	Quantifying Generalization in Reinforcement Learning. CoRR Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman
2017	—	`conf`	#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. NIPS Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
2017	J	`jnl`	Equivalence Between Policy Gradients and Soft Q-Learning. CoRR John Schulman, Pieter Abbeel, Xi Chen
2017	J	`jnl`	Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine
2017	J	`jnl`	Meta Learning Shared Hierarchies. CoRR Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
2017	J	`jnl`	Proximal Policy Optimization Algorithms. CoRR John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
2017	J	`jnl`	Teacher-Student Curriculum Learning. CoRR Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
2017	J	`jnl`	UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles. CoRR Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman
2017	—	`conf`	Variational Lossy Autoencoder. ICLR (Poster) Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. CoRR Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
2016	A*	`conf`	Benchmarking Deep Reinforcement Learning for Continuous Control. ICML Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
2016	J	`jnl`	Benchmarking Deep Reinforcement Learning for Continuous Control. CoRR Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
2016	J	`jnl`	Concrete Problems in AI Safety. CoRR Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, Dan Mané
2016	J	`jnl`	Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks. CoRR Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
2016	A*	`conf`	High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
2016	—	`conf`	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	OpenAI Gym. CoRR Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba
2016	—	`—`	Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs. John Schulman
2016	J	`jnl`	RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	Theano: A Python framework for fast computation of mathematical expressions. CoRR 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
2016	—	`conf`	VIME: Variational Information Maximizing Exploration. NIPS Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
2016	J	`jnl`	Variational Lossy Autoencoder. CoRR Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2015	—	`conf`	Gradient Estimation Using Stochastic Computation Graphs. NIPS John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
2015	J	`jnl`	Gradient Estimation Using Stochastic Computation Graphs. CoRR John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
2015	A*	`conf`	Trust Region Policy Optimization. ICML John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz
2015	J	`jnl`	Trust Region Policy Optimization. CoRR John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
2014	A*	`conf`	Gaussian belief space planning with discontinuities in sensing domains. ICRA Sachin Patil, Yan Duan, John Schulman, Ken Goldberg, Pieter Abbeel
2014	J	`jnl`	Motion planning with sequential convex optimization and convex collision checking. Int. J. Robotics Res. John Schulman, Yan Duan, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel
2014	A*	`conf`	Planning locally optimal, curvature-constrained trajectories in 3D using sequential convex optimization. ICRA Yan Duan, Sachin Patil, John Schulman, Kenneth Y. Goldberg, Pieter Abbeel
2014	C	`conf`	Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation. WAFR Sachin Patil, Gregory Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel
2013	A	`conf`	A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario. IROS John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel
2013	—	`conf`	Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization. Robotics: Science and Systems John Schulman, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Pieter Abbeel
2013	C	`conf`	Learning from Demonstrations Through the Use of Non-rigid Registration. ISRR John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel
2013	A	`conf`	Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles. IROS Alex X. Lee, Yan Duan, Sachin Patil, John Schulman, Zoe McCarthy, Jur van den Berg, Ken Goldberg, Pieter Abbeel
2013	A*	`conf`	Tracking deformable objects with point clouds. ICRA John Schulman, Alex X. Lee, Jonathan Ho, Pieter Abbeel