7 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
On the Way to LLM Personalization: Learning to Remember User Conversations
Lucie Charlotte Magister, Katherine Metcalf, Yizhe Zhang +1
Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to…
PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories
Stephane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald +1
Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs to infer pref…
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization
Yong Lin, Skyler Seto, Maartje ter Hoeve +6
Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. Central to RLHF is learning a reward function for scor…
Hindsight PRIORs for Reward Learning from Human Preferences
Mudit Verma, Katherine Metcalf
Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning a reward from preference feedback over policy behaviors. Current appro…
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Katherine Metcalf, Miguel Sarabia, Natalie Mackraz +1
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that…