Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback. - Rohin Shah; Steven H. Wang; Cody Wild; Stephanie Milani; Anssi Kanervisto; Vinicius G. Goecks; Nicholas R. Waytowich; David Watkins-Valls; Bharat Prakash; Edmund Mills; Divyansh Garg; Alexander Fries; et al. (NeurIPS (Competition and Demos), 2021)

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. - Mantas Mazeika; Eric Tang; Andy Zou; Steven Basart; Jun Shern Chan; Dawn Song; David A. Forsyth; Jacob Steinhardt; Dan Hendrycks (NeurIPS, 2022)

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback. - Rohin Shah; Steven H. Wang; Cody Wild; Stephanie Milani; Anssi Kanervisto; Vinicius G. Goecks; Nicholas R. Waytowich; David Watkins-Valls; Bharat Prakash; Edmund Mills; Divyansh Garg; Alexander Fries; et al. (CoRR, 2022)

Learning from Natural Language Feedback. - Jérémy Scheurer; Jon Ander Campos; Jun Shern Chan; Angelica Chen; Kyunghyun Cho; Ethan Perez (CoRR, 2022)

Few-shot Adaptation Works with UnpredicTable Data. - Jun Shern Chan; Michael Pieler; Jonathan Jao; Jérémy Scheurer; Ethan Perez (ACL (1), 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (ICML, 2023)

Improving Code Generation by Training with Natural Language Feedback. - Angelica Chen; Jérémy Scheurer; Tomasz Korbak; Jon Ander Campos; Jun Shern Chan; Samuel R. Bowman; Kyunghyun Cho; Ethan Perez (CoRR, 2023)

Training Language Models with Language Feedback at Scale. - Jérémy Scheurer; Jon Ander Campos; Tomasz Korbak; Jun Shern Chan; Angelica Chen; Kyunghyun Cho; Ethan Perez (CoRR, 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Jonathan Ng; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (CoRR, 2023)

Learning from Natural Language Feedback. - Angelica Chen; Jérémy Scheurer; Jon Ander Campos; Tomasz Korbak; Jun Shern Chan; Samuel R. Bowman; Kyunghyun Cho; Ethan Perez (Trans. Mach. Learn. Res., 2024)

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering. - Jun Shern Chan; Neil Chowdhury; Oliver Jaffe; James Aung; Dane Sherburn; Evan Mays; Giulio Starace; Kevin Liu; Leon Maksin; Tejal Patwardhan; Aleksander Madry; Lilian Weng (ICLR, 2025)

PaperBench: Evaluating AI's Ability to Replicate AI Research. - Giulio Starace; Oliver Jaffe; Dane Sherburn; James Aung; Jun Shern Chan; Leon Maksin; Rachel Dias; Evan Mays; Benjamin Kinsella; Wyatt Thompson; Johannes Heidecke; Amelia Glaese; et al. (CoRR, 2025)