This article details Tencent Search's exploration of large‑model‑driven ranking, covering the evolution from traditional keyword retrieval to RAG‑based AI search, the multi‑stage AI ranking architecture (L0‑L5), model training pipelines, distillation, synthetic data generation, and future research directions.
The paper describes transforming a tightly coupled, multi‑stage video search ranking pipeline into a modular, end‑to‑end large‑model architecture that decouples recall, employs a graph‑engine parallel framework and elastic compute allocation, thereby boosting performance, flexibility, personalization and lowering long‑term operational costs.
The article outlines how video search ranking has shifted from a tightly‑coupled multi‑stage cascade to an extensible, end‑to‑end, model‑centric framework called Rankflow, leveraging large‑model inference, decoupled recall, fine‑grained parallelism, and elastic compute allocation to boost performance, flexibility, and maintainability while paving the way for future retrieval‑augmented generation integration.
