Jacob Hilton

According to our database, Jacob Hilton authored at least 12 papers between 2016 and 2023.

Collaborative distances:

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

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

Teaching Models to Express Their Uncertainty in Words.

Stephanie Lin, Jacob Hilton, Owain Evans

Trans. Mach. Learn. Res., 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

TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Stephanie Lin, Jacob Hilton, Owain Evans

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 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

Phasic Policy Gradient.

Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman

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

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

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

2016

The Topological Pigeonhole Principle for Ordinals.

Jacob Hilton

J. Symb. Log., 2016