We introduce a novel training methodology named zELO, which optimizes retrieval performance via the analysis that ranking tasks are statically equivalent to a Thurstone model. Based on the zELO method, we use unsupervised data in order train a suite of state-of-the-art open-weight reranker models: zerank-1 and zerank-1-small. These models achieve the highest retrieval scores in multiple domains, including finance, legal, code, and STEM, outperforming closed-source proprietary rerankers on both NDCG@10 and Recall. These models also demonstrate great versatility, maintaining their 0-shot performance on out-of-domain and private customer datasets. The training data included 112,000 queries and 100 documents per query, and was trained end-to-end from unannotated queries and documents in less than 10,000 H100-hours.
@article{arxiv.2509.12541,
title = {zELO: ELO-inspired Training Method for Rerankers and Embedding Models},
author = {Nicholas Pipitone and Ghita Houir Alami and Advaith Avadhanam and Anton Kaminskyi and Ashley Khoo},
journal= {arXiv preprint arXiv:2509.12541},
year = {2025}
}