zELO: ELO-inspired Training Method for Rerankers and Embedding Models
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909790228185088 |
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| author | Pipitone, Nicholas Alami, Ghita Houir Avadhanam, Advaith Kaminskyi, Anton Khoo, Ashley |
| author_facet | Pipitone, Nicholas Alami, Ghita Houir Avadhanam, Advaith Kaminskyi, Anton Khoo, Ashley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | zELO: ELO-inspired Training Method for Rerankers and Embedding Models Pipitone, Nicholas Alami, Ghita Houir Avadhanam, Advaith Kaminskyi, Anton Khoo, Ashley Artificial Intelligence 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. |
| title | zELO: ELO-inspired Training Method for Rerankers and Embedding Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12541 |