LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

Real-world decision-making often involves competing goals --- like performance versus efficiency --- where defining a single reward function is difficult or impossible. LEMUR addresses this by combining multi-objective reinforcement learning with preference-based feedback, letting an agent learn from multiple humans\' preferences rather than requiring predefined reward functions. It jointly learns policies and objective-specific reward models, enabling agents to balance tradeoffs adaptively. This approach is valuable for real-world RL applications like robotics, resource allocation, or personalized systems, where human feedback naturally encodes competing priorities that are hard to hand-engineer. Authors: Manith Adikari, Bei Peng, Samuele Vinanzi, Angelo Cangelosi Paper: https://arxiv.org/abs/2607.29559v1