UniRank: A Unified Framework for Efficient Multi-Objective LLM Ranking in Industrial Search
This paper proposes UniRank, a Homogeneous Scaling framework that consolidates multiple ranking objectives into a single, unified generative model and introduces two paradigmatic innovations: Parallel Prediction via Task-Specific Anchors and Reinforcement Learning Optimization with Posterior CoT.