Adam Gleave

According to our database, Adam Gleave authored at least 44 papers between 2016 and 2026.

Collaborative distances:

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

2026

The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes.

Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave, Chris Cundy

CoRR, February, 2026

Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution.

Matthew Kowal, Goncalo Paulo, Louis Jaburi, Tom Tseng, Lev E. McKinney, Stefan Heimersheim, Aaron David Tucker, Adam Gleave, Kellin Pelrine

CoRR, February, 2026

Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks.

Lukas Struppek, Adam Gleave, Kellin Pelrine

CoRR, February, 2026

Large language models can effectively convince people to believe conspiracies.

Thomas Costello, Kellin Pelrine, Matthew Kowal, Antonio A. Arechar, Jean-François Godbout, Adam Gleave, David Rand, Gordon Pennycook

CoRR, January, 2026

STACK: Adversarial Attacks on LLM Safeguard Pipelines.

Ian R. McKenzie, Oskar John Hollinsworth, Tom Tseng, Xander Davies, Stephen Casper, Aaron David Tucker, Robert Kirk, Adam Gleave

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility.

Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Julius Broomfield, Adam Gleave, Kellin Pelrine

CoRR, July, 2025

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models.

Ann-Kathrin Dombrowski, Dillon Bowen, Adam Gleave, Chris Cundy

CoRR, July, 2025

The Singapore Consensus on Global AI Safety Research Priorities.

Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, Sören Mindermann, Vanessa Wilfred, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Siméon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnès Delaborde, Nouha Dziri, Francisco Eiras, Joshua Engels, Jinyu Fan, Adam Gleave, Noah Goodman, Fynn Heide, Johannes Heidecke, Dan Hendrycks, Cyrus Hodes, Bryan Low Kian Hsiang, Minlie Huang, Sami Jawhar, Wang Jingyu, Adam Tauman Kalai, Meindert Kamphuis, Mohan S. Kankanhalli, Subhash Kantamneni, Mathias Bonde Kirk, Thomas Kwa, Jeffrey Ladish, Kwok-Yan Lam, Wan Lee Sie, Taewhi Lee, Xiaojian Li, Jiajun Liu, Chaochao Lu, Yifan Mai, Richard Mallah, Julian Michael, Nick Moës, Simon Möller, Kihyuk Nam, Kwan Yee Ng, Mark Nitzberg, Besmira Nushi, Seán Ó hÉigeartaigh, Alejandro Ortega, Pierre Peigné, James Petrie, Benjamin Prud'homme, Reihaneh Rabbany, Nayat Sánchez-Pi, Sarah Schwettmann, Buck Shlegeris, Saad Siddiqui, Aradhana Sinha, Martín Soto, Cheston Tan, Dong Ting, William-Chandra Tjhi, Robert Trager, Brian Tse, Anthony Tung K. H., John Willes, Denise Wong, Wei Xu, Rongwu Xu, Yi Zeng, HongJiang Zhang, Djordje Zikelic

CoRR, June, 2025

Interpreting learned search: finding a transition model and value function in an RNN that plays Sokoban.

Mohammad Taufeeque, Aaron David Tucker, Adam Gleave, Adrià Garriga-Alonso

CoRR, June, 2025

It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics.

Matthew Kowal, Jasper Timm, Jean-François Godbout, Thomas Costello, Antonio A. Arechar, Gordon Pennycook, David Rand, Adam Gleave, Kellin Pelrine

CoRR, June, 2025

Preference Learning with Lie Detectors can Induce Honesty or Evasion.

Chris Cundy, Adam Gleave

CoRR, May, 2025

AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations.

Dillon Bowen, Ann-Kathrin Dombrowski, Adam Gleave, Chris Cundy

CoRR, March, 2025

Multi-Agent Risks from Advanced AI.

Lewis Hammond, Alan Chan, Jesse Clifton, Jason Hoelscher-Obermaier, Akbir Khan, Euan McLean, Chandler Smith, Wolfram Barfuss, Jakob N. Foerster, Tomas Gavenciak, The Anh Han, Edward Hughes, Vojtech Kovarík, Jan Kulveit, Joel Z. Leibo, Caspar Oesterheld, Christian Schröder de Witt, Nisarg Shah, Michael P. Wellman, Paolo Bova, Theodor Cimpeanu, Carson Ezell, Quentin Feuillade-Montixi, Matija Franklin, Esben Kran, Igor Krawczuk, Max Lamparth, Niklas Lauffer, Alexander Meinke, Sumeet Ramesh Motwani, Anka Reuel, Vincent Conitzer, Michael Dennis, Iason Gabriel, Adam Gleave, Gillian K. Hadfield, Nika Haghtalab, Atoosa Kasirzadeh, Sébastien Krier, Kate Larson, Joel Lehman, David C. Parkes, Georgios Piliouras, Iyad Rahwan

CoRR, February, 2025

Scaling Trends in Language Model Robustness.

Nikolaus H. R. Howe, Ian R. McKenzie, Oskar John Hollinsworth, Michal Zajac, Tom Tseng, Aaron David Tucker, Pierre-Luc Bacon, Adam Gleave

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility.

Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Tom Tseng, Julius Broomfield, Adam Gleave, Kellin Pelrine

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Can Go AIs Be Adversarially Robust?

Tom Tseng, Euan McLean, Kellin Pelrine, Tony Tong Wang, Adam Gleave

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

Scaling Trends for Data Poisoning in LLMs.

Dillon Bowen, Brendan Murphy, Will Cai, David Khachaturov, Adam Gleave, Kellin Pelrine

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Scaling Laws for Data Poisoning in LLMs.

Dillon Bowen, Brendan Murphy, Will Cai, David Khachaturov, Adam Gleave, Kellin Pelrine

CoRR, 2024

Exploring Scaling Trends in LLM Robustness.

Nikolaus H. R. Howe, Michal Zajac, Ian R. McKenzie, Oskar John Hollinsworth, Tom Tseng, Pierre-Luc Bacon, Adam Gleave

CoRR, 2024

Planning behavior in a recurrent neural network that plays Sokoban.

Adrià Garriga-Alonso, Mohammad Taufeeque, Adam Gleave

CoRR, 2024

Uncovering Latent Human Wellbeing in Language Model Embeddings.

Pedro Freire, ChengCheng Tan, Adam Gleave, Dan Hendrycks, Scott Emmons

CoRR, 2024

STARC: A General Framework For Quantifying Differences Between Reward Functions.

Joar Max Viktor Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Exploiting Novel GPT-4 APIs.

Kellin Pelrine, Mohammad Taufeeque, Michal Zajac, Euan McLean, Adam Gleave

CoRR, 2023

On The Fragility of Learned Reward Functions.

Lev McKinney, Yawen Duan, David Krueger, Adam Gleave

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

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning.

Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave

Proceedings of the International Conference on Machine Learning, 2023

2022

Towards Trustworthy Machine Learning

Adam Gleave

PhD thesis, 2022

imitation: Clean Imitation Learning Implementations.

Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell

CoRR, 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

Calculus on MDPs: Potential Shaping as a Gradient.

Erik Jenner, Herke van Hoof, Adam Gleave

CoRR, 2022

Reducing Exploitability with Population Based Training.

Pavel Czempin, Adam Gleave

CoRR, 2022

Preprocessing Reward Functions for Interpretability.

Erik Jenner, Adam Gleave

CoRR, 2022

A Primer on Maximum Causal Entropy Inverse Reinforcement Learning.

Adam Gleave, Sam Toyer

CoRR, 2022

Uncertainty Estimation for Language Reward Models.

Adam Gleave, Geoffrey Irving

CoRR, 2022

2021

Stable-Baselines3: Reliable Reinforcement Learning Implementations.

Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann

J. Mach. Learn. Res., 2021

Quantifying Differences in Reward Functions.

Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Understanding Learned Reward Functions.

Eric J. Michaud, Adam Gleave, Stuart Russell

CoRR, 2020

DERAIL: Diagnostic Environments for Reward And Imitation Learning.

Pedro Freire, Adam Gleave, Sam Toyer, Stuart Russell

CoRR, 2020

Adversarial Policies: Attacking Deep Reinforcement Learning.

Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Inverse reinforcement learning for video games.

Aaron Tucker, Adam Gleave, Stuart Russell

CoRR, 2018

Active Inverse Reward Design.

Sören Mindermann, Rohin Shah, Adam Gleave, Dylan Hadfield-Menell

CoRR, 2018

Multi-task Maximum Entropy Inverse Reinforcement Learning.

Adam Gleave, Oliver Habryka

CoRR, 2018

2017

Making Compression Algorithms for Unicode Text.

Adam Gleave, Christian Steinruecken

Proceedings of the 2017 Data Compression Conference, 2017

2016

Firmament: Fast, Centralized Cluster Scheduling at Scale.

Ionel Gog, Malte Schwarzkopf, Adam Gleave, Robert N. M. Watson, Steven Hand

Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016