3 papers
cs.LG2026
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Seungyub Han, Hyungjin Kim, Jungwoo Lee
Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavior. To address this, we presen…
cs.LG2025
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
Taehyun Cho, Seokhun Ju, Seungyub Han +3
To design rewards that align with human goals, Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent technique for learning reward functions from human prefe…
cs.LG2025
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
Taehyun Cho, Seungyub Han, Seokhun Ju +3
Distributional reinforcement learning improves performance by capturing environmental stochasticity, but a comprehensive theoretical understanding of its effectiveness remains elus…