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cs.LG2025
Provably Optimal Reinforcement Learning under Safety Filtering
Donggeon David Oh, Duy P. Nguyen, Haimin Hu +2
Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits its application in safety-critica…
cs.LG2025
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
Kaiqu Liang, Haimin Hu, Ryan Liu +2
While Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning generative AI, we present empirical evidence that it can also cause severe, systematic misalig…