R$^2$A: Learning Persona Policies Through Persona Representation Learning and Runtime Alignment
Across 12 evaluation settings covering the four principles of the Accountable-Professional Persona studied in this work, R$^2$A overall outperforms both the base model and static Persona elicitation and results show that Persona Representation Learning is critical for preventing Runtime Alignment from producing behaviorally imbalanced policies and for achieving more stable Persona policy learning.
Mo-Han Zhang, Chengsong You, Xiao-Yu Cao et al.
· 0 citations