UniRank: A Unified Framework for Efficient Multi-Objective LLM Ranking in Industrial Search

Abstract

Multi-objective ranking serves as the backbone of industrial information retrieval, requiring a holistic assessment of documents across dimensions such as Relevance, Authority, and Recency. The prevailing industry paradigm relies on ensembles of specialized BERT-based models, which are costly to maintain and fundamentally limited in expressiveness by the modest model size of discriminative encoders. While Large Language Models (LLMs) offer a path to unified reasoning, their auto-regressive latency is prohibitive for real-time serving. In this paper, we propose UniRank, a Homogeneous Scaling framework that consolidates multiple ranking objectives into a single, unified generative model. To reconcile the conflict between deep reasoning and inference speed, we introduce two paradigmatic innovations: (1) Parallel Prediction via Task-Specific Anchors: We replace slow sequential decoding with a parallel mechanism that predicts all objective scores simultaneously in a single forward pass, achieving O(1) inference complexity. (2) Reinforcement Learning Optimization with Posterior CoT: We propose a novel training framework that synergizes Posterior Chain-of-Thought with GRPO. By forcing the model to generate rationales after scoring and aligning them via policy optimization, we effectively distill complex reasoning capabilities into the anchor tokens without incurring inference-time overhead. Extensive experiments on the MS MARCO benchmark and a massive industrial dataset from Tencent demonstrate that UniRank significantly outperforms BERT-Large and matches the performance of larger LLMs, all while maintaining strictly low latency. Online A/B testing further confirms its practical value, showing that UniRank simplifies the deployment pipeline while delivering a significant lift in core business metrics.