arxivcs.LG2026-06-25
Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search
Ping Liu, Qianqi Shen, Jianqiang Shen, Wenqiong Liu, Rajat Arora, Yunxiang Ren, et al.
Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present an end-to-end RLAIF (Reinforcement Learning from AI Feedback) framework to generate \emph{portable} job search queries, terms t…