3 citations · 10 across the 7 of their papers we have counts for
3 papers · 1 filter
Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization
Ryan Park, Darren J. Hsu, C. Brian Roland +5
Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a m…
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Rafael Rafailov, Yaswanth Chittepu, Ryan Park +5
Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and brittle process. In the…
From to : Your Language Model is Secretly a Q-Function
Rafael Rafailov, Joey Hejna, Ryan Park +1
Reinforcement Learning From Human Feedback (RLHF) has been critical to the success of the latest generation of generative AI models. In response to the complex nature of the classi…