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cs.LG2024
Active Preference Optimization for Sample Efficient RLHF
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano +1
Large Language Models (LLMs) aligned using Reinforcement Learning from Human Feedback (RLHF) have shown remarkable generation abilities in numerous tasks. However, collecting high-…
cs.LG2024
A Theoretical Framework for Partially Observed Reward-States in RLHF
Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano +1
The growing deployment of reinforcement learning from human feedback (RLHF) calls for a deeper theoretical investigation of its underlying models. The prevalent models of RLHF do n…