On-the-fly workload partitioning for integrated CPU/GPU architectures. - Younghyun Cho; Florian Negele; Seohong Park; Bernhard Egger 002; Thomas R. Gross (PACT, 2018)

Unsupervised Skill Discovery with Bottleneck Option Learning. - Jaekyeom Kim; Seohong Park; Gunhee Kim (ICML, 2021)

Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. - Seohong Park; Jaekyeom Kim; Gunhee Kim (NeurIPS, 2021)

Lipschitz-constrained Unsupervised Skill Discovery. - Seohong Park; Jongwook Choi; Jaekyeom Kim; Honglak Lee; Gunhee Kim (ICLR, 2022)

Constrained GPI for Zero-Shot Transfer in Reinforcement Learning. - Jaekyeom Kim; Seohong Park; Gunhee Kim (NeurIPS, 2022)

Predictable MDP Abstraction for Unsupervised Model-Based RL. - Seohong Park; Sergey Levine (ICML, 2023)

Controllability-Aware Unsupervised Skill Discovery. - Seohong Park; Kimin Lee; Youngwoon Lee; Pieter Abbeel (ICML, 2023)

HIQL: Offline Goal-Conditioned RL with Latent States as Actions. - Seohong Park; Dibya Ghosh; Benjamin Eysenbach; Sergey Levine (NeurIPS, 2023)

METRA: Scalable Unsupervised RL with Metric-Aware Abstraction. - Seohong Park; Oleh Rybkin; Sergey Levine (ICLR, 2024)

Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings. - Kevin Frans; Seohong Park; Pieter Abbeel; Sergey Levine (ICML, 2024)

Foundation Policies with Hilbert Representations. - Seohong Park; Tobias Kreiman; Sergey Levine (ICML, 2024)

Is Value Learning Really the Main Bottleneck in Offline RL? - Seohong Park; Kevin Frans; Sergey Levine; Aviral Kumar (NeurIPS, 2024)

Unsupervised-to-Online Reinforcement Learning. - Junsu Kim; Seohong Park; Sergey Levine (CoRR, 2024)

GHIL-Glue: Hierarchical Control with Filtered Subgoal Images. - Kyle Beltran Hatch; Ashwin Balakrishna; Oier Mees; Suraj Nair 003; Seohong Park; Blake Wulfe; Masha Itkina; Benjamin Eysenbach; Sergey Levine; Thomas Kollar; Benjamin Burchfiel (CoRR, 2024)

OGBench: Benchmarking Offline Goal-Conditioned RL. - Seohong Park; Kevin Frans; Benjamin Eysenbach; Sergey Levine (ICLR, 2025)

Flow Q-Learning. - Seohong Park; Qiyang Li; Sergey Levine (CoRR, 2025)

Diffusion Guidance Is a Controllable Policy Improvement Operator. - Kevin Frans; Seohong Park; Pieter Abbeel; Sergey Levine (CoRR, 2025)

Horizon Reduction Makes RL Scalable. - Seohong Park; Kevin Frans; Deepinder Mann; Benjamin Eysenbach; Aviral Kumar; Sergey Levine (CoRR, 2025)

Intention-Conditioned Flow Occupancy Models. - Chongyi Zheng; Seohong Park; Sergey Levine; Benjamin Eysenbach (CoRR, 2025)

Steering Your Diffusion Policy with Latent Space Reinforcement Learning. - Andrew Wagenmaker; Mitsuhiko Nakamoto; Yunchu Zhang; Seohong Park; Waleed Yagoub; Anusha Nagabandi; Abhishek Gupta 004; Sergey Levine (CoRR, 2025)