Improving search ranking with chess Elo scores
Hello HN, I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents. We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0. We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps: * Collect soft preferences between pairs of documents using an ensemble of LLMs. * Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into…
In plain words
ZeroEntropy provides search infrastructure for retrieval-augmented generation and AI agents, offering two reranker models—zerank-1 and zerank-1-small—designed for high-accuracy document ranking. The company trained these models using a novel pipeline inspired by chess Elo scoring, which collects pairwise document comparisons via LLM ensembles and applies Bradley-Terry rating systems to generate relevance scores. One model is open-source under Apache 2.0, and both are available through an API and HuggingFace.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello HN, I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents. We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0. We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps: * Collect soft preferences between pairs of documents using an ensemble of LLMs. * Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores. * Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE. You can try the models either through our API (https://docs.zeroentropy.dev/models), or via HuggingFace (https://huggingface.co/zeroentropy/zerank-1-small). We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon. Thank you!
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