title: AIMO-2 Winning Solution: Building State-of-the-Art Mathematical
    Reasoning Models with OpenMathReasoning dataset
summary: This paper presents our winning submission to the AI Mathematical Olympiad -
  Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art
  mathematical reasoning models relies on three key pillars. First, we create a
  large-scale dataset comprising 540K unique high-quality math problems,
  including olympiad-level problems, and their 3.2M long-reasoning solutions.
  Second, we develop a novel method to integrate code execution with long
  reasoning models through iterative training, generation, and quality filtering,
  resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we
  create a pipeline to train models to select the most promising solution from
  many candidates. We show that such generative solution selection (GenSelect)
  can significantly improve upon majority voting baseline. Combining these ideas,
  we train a series of models that achieve state-of-the-art results on
  mathematical reasoning benchmarks. To facilitate further research, we release
  our code, models, and the complete OpenMathReasoning dataset under a
  commercially permissive license.
paper_authors._id: 6809ba729786ec0ed3afe6af
paper_authors.name: Ivan Moshkov
paper_authors.status: claimed_verified
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paper_authors.fullname: Ivan Moshkov
paper_authors.type: user
paper_authors.user: imoshkov
paper_authors._id: 6809ba729786ec0ed3afe6b0
paper_authors.name: Darragh Hanley
paper_authors.status: claimed_verified
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paper_authors.fullname: Darragh Hanley
paper_authors.type: user
paper_authors.user: darragh
paper_authors._id: 6809ba729786ec0ed3afe6b1
paper_authors.name: Ivan Sorokin
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paper_authors.fullname: Ivan Sorokin
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paper_authors.user: sorokin
paper_authors._id: 6809ba729786ec0ed3afe6b2
paper_authors.name: Shubham Toshniwal
paper_authors.status: claimed_verified
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paper_authors.fullname: Shubham Toshniwal
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paper_authors.user: stoshniwal
paper_authors._id: 6809ba729786ec0ed3afe6b3
paper_authors.name: Christof Henkel
paper_authors._id: 6809ba729786ec0ed3afe6b4
paper_authors.name: Benedikt Schifferer
paper_authors._id: 6809ba729786ec0ed3afe6b5
paper_authors.name: Wei Du
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T08:00:13.624Z
paper_authors._id: 662fcd0314e87951eaded842
paper_authors.avatarUrl: /avatars/868eccf33126298bd47ecac9da810116.svg
paper_authors.fullname: Wei Du
paper_authors.type: user
paper_authors.user: woshiweidu
paper_authors._id: 6809ba729786ec0ed3afe6b6
paper_authors.name: Igor Gitman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:37:56.787Z
paper_authors._id: 6356f834e75acf88c902b538
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1666644004634-noauth.jpeg
paper_authors.fullname: Igor Gitman
paper_authors.type: user
paper_authors.user: igitman
paper_title: AIMO-2 Winning Solution: Building State-of-the-Art Mathematical
    Reasoning Models with OpenMathReasoning dataset
paper_summary: This paper presents our winning submission to the AI Mathematical Olympiad -
  Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art
  mathematical reasoning models relies on three key pillars. First, we create a
  large-scale dataset comprising 540K unique high-quality math problems,
  including olympiad-level problems, and their 3.2M long-reasoning solutions.
  Second, we develop a novel method to integrate code execution with long
  reasoning models through iterative training, generation, and quality filtering,
  resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we
  create a pipeline to train models to select the most promising solution from
  many candidates. We show that such generative solution selection (GenSelect)
  can significantly improve upon majority voting baseline. Combining these ideas,
  we train a series of models that achieve state-of-the-art results on
  mathematical reasoning benchmarks. To facilitate further research, we release
  our code, models, and the complete OpenMathReasoning dataset under a
  commercially permissive license.