Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and off-Policy Data

The challenge of matching human preferences to big pretrained models has gained prominence in the study as these models have grown in performance. This alignment becomes particularly challenging when there are unavoidably poor behaviours in bigger datasets. For this issue, reinforcement learning from human input, or RLHF has become popular. RLHF approaches use human preferences […]

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