Rank1: Test-Time Compute for Reranking in Information Retrieval
Authors: name: Orion Weller, name: Kathryn Ricci, name: Eugene Yang, name: Andrew Yates, name: Dawn Lawrie, name: Benjamin Van Durme

We introduce Rank1, the first reranking model trained to take advantage of
test-time compute. Rank1 demonstrates the applicability within retrieval of
using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for
distillation in order to rapidly improve the performance of a smaller model. We
gather and open-source a dataset of more than 600,000 examples of R1 reasoning
traces from queries and passages in MS MARCO. Models trained on this dataset
show: (1) state-of-the-art performance on advanced reasoning and instruction
following datasets; (2) work remarkably well out of distribution due to the
ability to respond to user-input prompts; and (3) have explainable reasoning
chains that can be given to users or RAG-based systems. Further, we demonstrate
that quantized versions of these models retain strong performance while using
less compute/memory. Overall, Rank1 shows that test-time compute allows for a
fundamentally new type of explainable and performant reranker model for search.