#preference-learning

#preference-learning

Vik's research focuses on how robots can learn from minimal human feedback, adapting without the need for large datasets.

The paper presents Direct Nash Optimization, enhancing large language model training by utilizing pair-wise preferences instead of traditional reward maximization.

Direct Preference Optimization (DPO) is introduced as an effective method for preference learning, demonstrated through rigorous experimental validation.