Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers
A single model scale challenges the flexibility of a production retrieval system: some settings need it faster, others need a smaller index, and the right trade-off changes with the workload. In the context of information retrieval (IR), a transformer-based model can be made smaller in three ways---using fewer layers, passing fewer tokens through the upper layers, or producing a shorter embedding---and each way saves a different compute resource. These options have been studied one at a time, each as its own method with its own code and training setup, which makes them hard to combine or adapt to a new model. We present~\ours to bring all three under one simple abstraction: a single objec...
| Authors | Yu Wang, Shengyao Zhuang, Xueguang Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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