1 citations · 2 across the 15 of their papers we have counts for
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Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
Hyungkyu Kang, Byeongchan Kim, Min-hwan Oh
Offline goal-conditioned reinforcement learning (GCRL) provides a practical framework for obtaining goal-reaching policies from fixed datasets. However, learning a reliable goal-co…
Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
Deokgyu Yoon, Hyungkyu Kang, Joongkyu Lee +4
Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely used PPO surrogate objective…
Block-Sphere Vector Quantization
Heesang Ann, Joongkyu Lee, Min-hwan Oh
Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compressed inference. Recent rotation-b…
Peng's Q() for Conservative Value Estimation in Offline Reinforcement Learning
Byeongchan Kim, Min-hwan Oh
We propose a model-free offline multi-step reinforcement learning (RL) algorithm, Conservative Peng's Q() (CPQL). Our algorithm adapts the Peng's Q() (PQL) operator for conse…
RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
Byeongchan Kim, Arijit Sehanobish, Avinava Dubey +2
We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable modulation functions . Th…
Exploration via Feature Perturbation in Contextual Bandits
Seouh-won Yi, Min-hwan Oh
We propose feature perturbation, a simple yet effective exploration strategy for contextual bandits that injects randomness directly into feature inputs, instead of randomizing unk…
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
Joongkyu Lee, Seouh-won Yi, Min-hwan Oh
We study online preference-based reinforcement learning (PbRL) with the goal of improving sample efficiency. While a growing body of theoretical work has emerged-motivated by PbRL'…
Learning Uncertainty-Aware Temporally-Extended Actions
Joongkyu Lee, Seung Joon Park, Yunhao Tang +1
In reinforcement learning, temporal abstraction in the action space, exemplified by action repetition, is a technique to facilitate policy learning through extended actions. Howeve…