Seungho Baek — accepted AI-conference papers

2026

Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL

Jongchan Park, Seungjun Oh, Seungho Baek, Yusung Kim

ICML 2026poster

Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation for downstream control tasks. Despite recent progress, we argue that current off-policy URL methods are limited by two critical, overlooked bottlenecks:…

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2025

Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Seungho Baek, taegeon park, Jongchan Park, Seungjun Oh, Yusung Kim

ICML 2025poster

Existing offline hierarchical reinforcement learning methods rely on high-level policy learning to generate subgoal sequences. However, their efficiency degrades as task horizons increase, and they lack effective strategies for stitching useful state transitions across different trajectories. We pro…

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