Quantifying Generalization in Reinforcement Learning. - Karl Cobbe; Oleg Klimov; Christopher Hesse; Taehoon Kim; John Schulman (ICML, 2019)

Leveraging Procedural Generation to Benchmark Reinforcement Learning. - Karl Cobbe; Christopher Hesse; Jacob Hilton; John Schulman (ICML, 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; et al. (NeurIPS (Competition and Demos), 2020)

Phasic Policy Gradient. - Karl Cobbe; Jacob Hilton; Oleg Klimov; John Schulman (ICML, 2021)

Training Verifiers to Solve Math Word Problems. - Karl Cobbe; Vineet Kosaraju; Mohammad Bavarian; Mark Chen 003; Heewoo Jun; Lukasz Kaiser; Matthias Plappert; Jerry Tworek; Jacob Hilton; Reiichiro Nakano; Christopher Hesse; John Schulman (CoRR, 2021)

WebGPT: Browser-assisted question-answering with human feedback. - Reiichiro Nakano; Jacob Hilton; Suchir Balaji; Jeff Wu 003; Long Ouyang; Christina Kim; Christopher Hesse; Shantanu Jain; Vineet Kosaraju; William Saunders; Xu Jiang; Karl Cobbe; et al. (CoRR, 2021)

Batch size-invariance for policy optimization. - Jacob Hilton; Karl Cobbe; John Schulman (NeurIPS, 2022)

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 (ICLR, 2024)