title: ReasonIR: Training Retrievers for Reasoning Tasks
summary: 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.
paper_authors._id: 68118a9f4570c2ba44bf4418
paper_authors.name: Rulin Shao
paper_authors.status: admin_assigned
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paper_authors.fullname: Rulin Shao
paper_authors.type: user
paper_authors.user: rulins
paper_authors._id: 68118a9f4570c2ba44bf4419
paper_authors.name: Rui Qiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:37:39.498Z
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paper_authors.fullname: Rui Qiao
paper_authors.type: user
paper_authors.user: qiaoruiyt
paper_authors._id: 68118a9f4570c2ba44bf441a
paper_authors.name: Varsha Kishore
paper_authors._id: 68118a9f4570c2ba44bf441b
paper_authors.name: Niklas Muennighoff
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-30T10:02:27.811Z
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paper_authors.fullname: Niklas Muennighoff
paper_authors.type: user
paper_authors.user: Muennighoff
paper_authors._id: 68118a9f4570c2ba44bf441c
paper_authors.name: Xi Victoria Lin
paper_authors._id: 68118a9f4570c2ba44bf441d
paper_authors.name: Daniela Rus
paper_authors._id: 68118a9f4570c2ba44bf441e
paper_authors.name: Bryan Kian Hsiang Low
paper_authors._id: 68118a9f4570c2ba44bf441f
paper_authors.name: Sewon Min
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-30T10:01:46.233Z
paper_authors._id: 63a76d0de27a6dbd485fe863
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a76d0de27a6dbd485fe863/qJJwHOuvyQGq1o0KscOF_.jpeg
paper_authors.fullname: Sewon Min
paper_authors.type: user
paper_authors.user: sewon
paper_authors._id: 68118a9f4570c2ba44bf4420
paper_authors.name: Wen-tau Yih
paper_authors._id: 68118a9f4570c2ba44bf4421
paper_authors.name: Pang Wei Koh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-30T10:01:57.373Z
paper_authors._id: 641b4263abfce26bcf7b27de
paper_authors.avatarUrl: /avatars/e91b4205e4f74b0dd8c333c23203a924.svg
paper_authors.fullname: Pang Wei Koh
paper_authors.type: user
paper_authors.user: pangwei
paper_authors._id: 68118a9f4570c2ba44bf4422
paper_authors.name: Luke Zettlemoyer
paper_title: ReasonIR: Training Retrievers for Reasoning Tasks
paper_summary: 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.
paper_ai_summary: ReasonIR-8B, a retriever trained with synthetic challenging queries, achieves state-of-the-art results in reasoning-intensive information retrieval and RAG tasks, improving performance and computational efficiency.
paper_ai_keywords: retriever synthetic data generation nDCG@10 BRIGHT RAG tasks MMLU GPQA closed-book baseline test-time compute LLM reranker