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 ...
