# You Only Evaluate Once: A Tree-based Rerank Method at Meituan

arXiv: https://arxiv.org/abs/2508.14420 · HTML: https://mlsanity.com/paper/2508.14420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction](https://mlsanity.com/paper/2511.21394/) (2025)
- [FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation](https://mlsanity.com/paper/2603.02565/) (2026)

## Abstract (arXiv)

Reranking plays a crucial role in modern recommender systems by capturing the mutual influences within the list. Due to the inherent challenges of combinatorial search spaces, most methods adopt a two-stage search paradigm: a simple General Search Unit (GSU) efficiently reduces the candidate space, and an Exact Search Unit (ESU) effectively selects the optimal sequence. These methods essentially involve making trade-offs between effectiveness and efficiency, while suffering from a severe inconsistency problem, that is, the GSU often misses high-value lists from ESU. To address this problem, we propose YOLOR, a one-stage reranking method that removes the GSU while retaining only the ESU. Specifically, YOLOR includes: (1) a Tree-based Context Extraction Module (TCEM) that hierarchically aggregates multi-scale contextual features to achieve "list-level effectiveness", and (2) a Context Cache Module (CCM) that enables efficient feature reuse across candidate permutations to achieve "permutation-level efficiency". Extensive experiments across public and industry datasets validate YOLOR's performance, and we have successfully deployed YOLOR on the Meituan food delivery platform.