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cs.LG2025★ 1 cited
Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli +3
Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a we…
cs.LG2025
Influencing Humans to Conform to Preference Models for RLHF
Stephane Hatgis-Kessell, W. Bradley Knox, Serena Booth +1
Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assuming, implicitly or explicitly, a model o…