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arXiv · 2509.12541

zELO: ELO-inspired Training Method for Rerankers and Embedding Models

Nicholas Pipitone·Ghita Houir Alami·Advaith Avadhanam·Anton Kaminskyi·Ashley Khoo
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Abstract

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.

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Nicholas Pipitone, Ghita Houir Alami, Advaith Avadhanam, Anton Kaminskyi, Ashley Khoo. 2025-09-16. zELO: ELO-inspired Training Method for Rerankers and Embedding Models. https://arxiv.org/abs/2509.12541

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