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cs.AI2026
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Manith Adikari, Bei Peng, Samuele Vinanzi +1
Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. However, real-world decision-making tasks often involve multiple, c…
cs.AI2025
The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang +8
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental…