Tony T. Wang

According to our database, Tony T. Wang authored at least 7 papers between 2022 and 2024.

Collaborative distances:

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

2024

Can Go AIs be adversarially robust?

Tom Tseng, Euan McLean, Kellin Pelrine, Tony T. Wang, Adam Gleave

CoRR, 2024

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation.

Danny Halawi, Alexander Wei, Eric Wallace, Tony Tong Wang, Nika Haghtalab, Jacob Steinhardt

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell

Trans. Mach. Learn. Res., 2023

Forbidden Facts: An Investigation of Competing Objectives in Llama-2.

Tony T. Wang, Miles Wang, Kaivalya Hariharan, Nir Shavit

CoRR, 2023

Cliff-Learning.

Tony T. Wang, Igor Zablotchi, Nir Shavit, Jonathan S. Rosenfeld

CoRR, 2023

Adversarial Policies Beat Superhuman Go AIs.

Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell

Proceedings of the International Conference on Machine Learning, 2023

2022

Adversarial Policies Beat Professional-Level Go AIs.

Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell

CoRR, 2022