Publications

Ryan Sullivan, Raghav Gupta, Ryan Sullivan, Yunxuan Li, Samrat Phatale, Abhinav Rastogi (2025). Robust Multi-Objective Preference Alignment with Online DPO. In AAAI 2025.

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Ryan Sullivan, Ryan Sullivan, Ryan Pégoud, Ameen Ur Rahmen, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P. Dickerson (2024). Syllabus: Portable Curricula for Reinforcement Learning Agents. arXiv preprint.

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Ryan Sullivan, Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey (2024). Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning. In EMNLP 2024.

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Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P. Dickerson, Joseph Suarez (2023). Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks. In NeurIPS 2023.

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Joseph Suarez, David Bloomin, Kyoung Whan Choe, Hao Xiang Li, Ryan Sullivan, Nishaanth Kanna Ravichandran, Daniel Scott, Rose S Shuman, Herbie Bradley, Louis Castricato, Phillip Isola, Kirsty You, Yuhao Jiang, Qimai Li, Jiaxin Chen, Xiaolong Zhu (2023). Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. In NeurIPS 2023.

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Sanghyun Son, Laura Yu Zheng, Ryan Sullivan, Yi-Ling Qiao, Ming Lin (2023). Gradient Informed Proximal Policy Optimization. In NeurIPS 2023.

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Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson (2022). Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. In ICML 2022.

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J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Rodriguez Perez, Caroline Horsch, Clemens Dieffendahl, Niall L. Williams, Yashas Lokesh, Praveen Ravi (2021). PettingZoo: A Standard API for Multi-Agent Reinforcement Learning. In NeurIPS 2021.

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