Reproduce “R$^2$A: Learning Persona Policies Through Persona Representation Learning and Runtime Alignment” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Mohan Zhang, Chengsong You, Xiaoyu Cao, Zhen Sun, Xiaohan Jia, Junwei Zhou, Yongchao Chen
R²A trains an LLM to choose persona behaviors from context without a persona prompt at inference. It teaches purpose, principles, and examples, then uses task feedback to train behavior selection and response execution. On Qwen3-8B, it beats the base in 10 of 12 settings and static prompting in 9, ties in 1, and loses in 2, for one persona across four principles.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.