Firmament: Fast, Centralized Cluster Scheduling at Scale. - Ionel Gog; Malte Schwarzkopf; Adam Gleave; Robert N. M. Watson; Steven Hand 001 (OSDI, 2016)

Making Compression Algorithms for Unicode Text. - Adam Gleave; Christian Steinruecken (DCC, 2017)

Multi-task Maximum Entropy Inverse Reinforcement Learning. - Adam Gleave; Oliver Habryka (CoRR, 2018)

Active Inverse Reward Design. - Sören Mindermann; Rohin Shah; Adam Gleave; Dylan Hadfield-Menell (CoRR, 2018)

Inverse reinforcement learning for video games. - Aaron Tucker; Adam Gleave; Stuart Russell 001 (CoRR, 2018)

Adversarial Policies: Attacking Deep Reinforcement Learning. - Adam Gleave; Michael Dennis 001; Cody Wild; Neel Kant; Sergey Levine; Stuart Russell 001 (ICLR, 2020)

DERAIL: Diagnostic Environments for Reward And Imitation Learning. - Pedro Freire; Adam Gleave; Sam Toyer; Stuart Russell 001 (CoRR, 2020)

Understanding Learned Reward Functions. - Eric J. Michaud; Adam Gleave; Stuart Russell 001 (CoRR, 2020)

Quantifying Differences in Reward Functions. - Adam Gleave; Michael Dennis 001; Shane Legg; Stuart Russell 001; Jan Leike (ICLR, 2021)

Stable-Baselines3: Reliable Reinforcement Learning Implementations. - Antonin Raffin; Ashley Hill; Adam Gleave; Anssi Kanervisto; Maximilian Ernestus; Noah Dormann (J. Mach. Learn. Res., 2021)

Uncertainty Estimation for Language Reward Models. - Adam Gleave; Geoffrey Irving (CoRR, 2022)

A Primer on Maximum Causal Entropy Inverse Reinforcement Learning. - Adam Gleave; Sam Toyer (CoRR, 2022)

Preprocessing Reward Functions for Interpretability. - Erik Jenner; Adam Gleave (CoRR, 2022)

Reducing Exploitability with Population Based Training. - Pavel Czempin; Adam Gleave (CoRR, 2022)

Calculus on MDPs: Potential Shaping as a Gradient. - Erik Jenner; Herke van Hoof; Adam Gleave (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 001 (CoRR, 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 001 (CoRR, 2022)

Towards Trustworthy Machine Learning - Adam Gleave (2022)

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. - Joar Max Viktor Skalse; Matthew Farrugia-Roberts; Stuart Russell 001; Alessandro Abate; Adam Gleave (ICML, 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 001 (ICML, 2023)

On The Fragility of Learned Reward Functions. - Lev McKinney; Yawen Duan; David Krueger 001; Adam Gleave (CoRR, 2023)

Exploiting Novel GPT-4 APIs. - Kellin Pelrine; Mohammad Taufeeque; Michal Zajac 005; Euan McLean; Adam Gleave (CoRR, 2023)

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 (ICLR, 2024)

Uncovering Latent Human Wellbeing in Language Model Embeddings. - Pedro Freire; ChengCheng Tan; Adam Gleave; Dan Hendrycks; Scott Emmons (CoRR, 2024)

Planning behavior in a recurrent neural network that plays Sokoban. - Adrià Garriga-Alonso; Mohammad Taufeeque; Adam Gleave (CoRR, 2024)

Exploring Scaling Trends in LLM Robustness. - Nikolaus H. R. Howe; Michal Zajac 005; Ian R. McKenzie; Oskar Hollinsworth; Tom Tseng; Pierre-Luc Bacon; Adam Gleave (CoRR, 2024)

Scaling Laws for Data Poisoning in LLMs. - Dillon Bowen; Brendan Murphy; Will Cai; David Khachaturov; Adam Gleave; Kellin Pelrine (CoRR, 2024)

Scaling Trends for Data Poisoning in LLMs. - Dillon Bowen; Brendan Murphy; Will Cai; David Khachaturov; Adam Gleave; Kellin Pelrine (AAAI, 2025)

Can Go AIs Be Adversarially Robust? - Tom Tseng; Euan McLean; Kellin Pelrine; Tony Tong Wang; Adam Gleave (AAAI, 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 001; et al. (CoRR, 2025)

AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations. - Dillon Bowen; Ann-Kathrin Dombrowski; Adam Gleave; Chris Cundy (CoRR, 2025)

Preference Learning with Lie Detectors can Induce Honesty or Evasion. - Chris Cundy; Adam Gleave (CoRR, 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, 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, 2025)

The Singapore Consensus on Global AI Safety Research Priorities. - Yoshua Bengio; Tegan Maharaj; Luke Ong; Stuart Russell 001; Dawn Song; Max Tegmark; Lan Xue; Ya-Qin Zhang; Stephen Casper; Wan Sie Lee; Sören Mindermann; Vanessa Wilfred; et al. (CoRR, 2025)

STACK: Adversarial Attacks on LLM Safeguard Pipelines. - Ian R. McKenzie; Oskar Hollinsworth; Tom Tseng; Xander Davies; Stephen Casper; Aaron D. Tucker; Robert Kirk; Adam Gleave (CoRR, 2025)

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models. - Ann-Kathrin Dombrowski; Dillon Bowen; Adam Gleave; Chris Cundy (CoRR, 2025)

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility. - Brendan Murphy; Dillon Bowen; Shahrad Mohammadzadeh; Julius Broomfield; Adam Gleave; Kellin Pelrine (CoRR, 2025)