Joey Hong

I am a PhD Student advised by Professor Anca Dragan and Professor Sergey Levine, where I work on offline reinforcement learning.

Prior to joining my PhD program, I was an AI Resident at Google Research, where I worked on multi-task bandits as well as program synthesis.

Before that, I was graduated from Caltech where I worked with Professor Yisong Yue.

Email / Google Scholar / Github

Current Research

I'm currently interested in pushing the capabilities of offline reinforcement learning, specifically in applications that involve interacting with humans, through a mixture of theory and applied work.

	Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning Joey Hong, Anca Dragan Sergey Levine, ICLR, 2025 arXiv
	Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations Joey Hong, Sergey Levine, Anca Dragan NeurIPS Foundation Models for Decision Making Workshop, 2024 arXiv, slides
	Offline RL with Observation Histories: Analyzing and Improving Sample Complexity Joey Hong, Anca Dragan, Sergey Levine ICLR, 2024 arXiv
	Learning to Influence Human Behavior with Offline Reinforcement Learning Joey Hong, Sergey Levine, Anca Dragan NeurIPS, 2023 arXiv, website
	Confidence-Conditioned Value Functions for Offline Reinforcement Learning Joey Hong, Aviral Kumar, Sergey Levine ICLR, 2022 (oral) arXiv
	On the Sensitivity of Reward Inference to Misspecified Human Models Joey Hong, Kush Bhatia, Anca Dragan ICLR, 2022 (oral) arXiv
	When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine ICLR, 2021 arXiv, blog

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