Yongyuan Liang

Yongyuan (Cheryl) Liang's research focuses on building robust, versatile, and efficient intelligent agents with strong generalization capacity for AI systems.
She actively explores both theoretical frameworks and empirical findings, with specific research interests in:

Reinforcement Learning (robust RL, offline RL, IRL, RLHF)
Foundation models for policy learning (generative models, representation)
Trustworthy LLM agents for planning and reasoning

Email / Google Scholar / Github / Twitter

I join UMD CS as a PhD student, advised by Furong Huang and also work closely with Huazhe Xu (IIIS). I received my B.S. degree in Mathematics from Sun Yat-sen University, developing my interests in Stochastic Process and Game Theory.

I'm always happy to collaborate with graduate/undergraduate students. Please drop me an email if you'd like to have a (virtual) coffee chat :)
I'm looking for part-time/full-time internship opportunities. Feel free to reach out if you're interested in my research.

June' 24	ACE has been selected as long oral presentation in ICML 2024.
May' 24	Two papers to appear in ICML 2024.
Feb' 24	Have been awarded a Dean’s Fellowship.
Jan' 24	Three papers to appear in ICLR 2024, including two spotlights and one poster.

Selected Publications & Preprints

* denotes equal contributions; § indicates mentoring involvement.

	Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion Yongyuan Liang, Tingqiang Xu, Kaizhe Hu, Guangqi Jiang, Furong Huang, Huazhe Xu arXiv, 2024 Project Page / Paper / Code / Models & Dataset / Twitter
	Is poisoning a real threat to LLM alignment? Maybe more so than you think Pankayaraj Pathmanathan, Souradip Chakraborty, Xiangyu Liu, Yongyuan Liang, Furong Huang arXiv, 2024 ICML Workshop on Models of Human Feedback for AI Alignment, 2024 Paper / Code
	ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu ICML, 2024 (Oral - Top 1.5%) Project Page / Paper / Code / Twitter
	PREMIER-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Basu, Furong Huang ICML, 2024 NeurIPS Workshop FMDM, 2023 Project Page / Paper / Code / Twitter
	DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization Guowei Xu, Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Zhecheng Yuan, Tianying Ji, Yu Luo, Xiaoyu Liu, Jiaxin Yuan, Pu Hua, Shuzhen Li, Yanjie Ze, Hal Daumé III, Furong Huang, Huazhe Xu ICLR, 2024 (Spotlight - Top 5%)* CORL Workshop PRL, 2023 Project Page / Paper / Code / Twitter
	Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Xiangyu Liu, Benjamin Eysenbach, Tuomas Sandholm, Furong Huang, Stephen Marcus McAleer ICLR, 2024 ICML Workshop AdvML-Frontiers, 2023 Paper / Code / Twitter
	Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang NeurIPS, 2022 NeurIPS Workshop SafeRL, 2021 (Spotlight Talks) Paper / Code / Slides
	Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies Xiangyu Liu, Chenghao Deng, Yanchao Sun, Yongyuan Liang, Furong Huang ICLR, 2024 (Spotlight - Top 5%) NeurIPS Workshop MASEC, 2023 Project Page / Paper / Code / Twitter
	Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang ICLR, 2022 NeurIPS Workshop SafeRL, 2021 (Best Paper Award) Project Page / Paper / Code
	Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang ICLR, 2023 Paper / Code