Dataset Reset Policy Optimization (DR-PO): A Machine Learning Algorithm that Exploits a Generative Model’s Ability to Reset from Offline Data to Enhance RLHF from Preference-based Feedback

Reinforcement Learning (RL) continuously evolves as researchers explore methods to refine algorithms that learn from human feedback. This domain of learning algorithms deals with challenges in defining and optimizing reward functions critical for training models to perform various tasks ranging from gaming to language processing. A prevalent issue in this area is the inefficient use […]

The post Dataset Reset Policy Optimization (DR-PO): A Machine Learning Algorithm that Exploits a Generative Model’s Ability to Reset from Offline Data to Enhance …

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