ReasonIR: Training Retrievers for Reasoning Tasks
Authors: name: Rulin Shao, name: Rui Qiao, name: Varsha Kishore, name: Niklas Muennighoff, name: Xi Victoria Lin, name: Daniela Rus, name: Bryan Kian Hsiang Low, name: Sewon Min, name: Wen-tau Yih, name: Pang Wei Koh, name: Luke Zettlemoyer

We present ReasonIR-8B, the first retriever specifically trained for general
reasoning tasks. Existing retrievers have shown limited gains on reasoning
tasks, in part because existing training datasets focus on short factual
queries tied to documents that straightforwardly answer them. We develop a
synthetic data generation pipeline that, for each document, our pipeline
creates a challenging and relevant query, along with a plausibly related but
ultimately unhelpful hard negative. By training on a mixture of our synthetic
data and existing public data, ReasonIR-8B achieves a new state-of-the-art of
29.9 nDCG@10 without reranker and 36.9 nDCG@10 with reranker on BRIGHT, a
widely-used reasoning-intensive information retrieval (IR) benchmark. When
applied to RAG tasks, ReasonIR-8B improves MMLU and GPQA performance by 6.4%
and 22.6% respectively, relative to the closed-book baseline, outperforming
other retrievers and search engines. In addition, ReasonIR-8B uses test-time
compute more effectively: on BRIGHT, its performance consistently increases
with longer and more information-rich rewritten queries; it continues to
outperform other retrievers when combined with an LLM reranker. Our training
recipe is general and can be easily extended to future LLMs; to this end, we
open-source our code, data, and model.