title: OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
summary: Recent work has shown the immense potential of synthetically generated
  datasets for training large language models (LLMs), especially for acquiring
  targeted skills. Current large-scale math instruction tuning datasets such as
  MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed
  using outputs from closed-source LLMs with commercially restrictive licenses. A
  key reason limiting the use of open-source LLMs in these data generation
  pipelines has been the wide gap between the mathematical skills of the best
  closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on
  the recent progress in open-source LLMs, our proposed prompting novelty, and
  some brute-force scaling, we construct OpenMathInstruct-1, a math instruction
  tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by
  synthesizing code-interpreter solutions for GSM8K and MATH, two popular math
  reasoning benchmarks, using the recently released and permissively licensed
  Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of
  OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which
  is competitive with the best gpt-distilled models. We release our code, models,
  and the OpenMathInstruct-1 dataset under a commercially permissive license.
paper_authors._id: 65cec22a6da01df53622b5f8
paper_authors.name: Shubham Toshniwal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T08:43:37.415Z
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paper_authors.fullname: Shubham Toshniwal
paper_authors.type: user
paper_authors.user: shtoshni
paper_authors._id: 65cec22a6da01df53622b5f9
paper_authors.name: Ivan Moshkov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:03:29.189Z
paper_authors._id: 65cd2166c52afc14cebc171a
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paper_authors.fullname: Ivan Moshkov
paper_authors.type: user
paper_authors.user: imoshkov
paper_authors._id: 65cec22a6da01df53622b5fa
paper_authors.name: Sean Narenthiran
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:03:36.036Z
paper_authors._id: 62ce959a539556d82c9ebc59
paper_authors.avatarUrl: /avatars/b4ef43a356c24dcbb3ba6a26aab6e12a.svg
paper_authors.fullname: Sean Narenthiran
paper_authors.type: user
paper_authors.user: SeanNaren
paper_authors._id: 65cec22a6da01df53622b5fb
paper_authors.name: Daria Gitman
paper_authors._id: 65cec22a6da01df53622b5fc
paper_authors.name: Fei Jia
paper_authors._id: 65cec22a6da01df53622b5fd
paper_authors.name: Igor Gitman
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:04:03.985Z
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: OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
paper_summary: Recent work has shown the immense potential of synthetically generated
  datasets for training large language models (LLMs), especially for acquiring
  targeted skills. Current large-scale math instruction tuning datasets such as
  MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed
  using outputs from closed-source LLMs with commercially restrictive licenses. A
  key reason limiting the use of open-source LLMs in these data generation
  pipelines has been the wide gap between the mathematical skills of the best
  closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on
  the recent progress in open-source LLMs, our proposed prompting novelty, and
  some brute-force scaling, we construct OpenMathInstruct-1, a math instruction
  tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by
  synthesizing code-interpreter solutions for GSM8K and MATH, two popular math
  reasoning benchmarks, using the recently released and permissively licensed
  Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of
  OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which
  is competitive with the best gpt-distilled models. We release our code, models,
  and the OpenMathInstruct-1 dataset under a commercially permissive license.
paper_ai_summary: A new math instruction tuning dataset, OpenMathInstruct-1, leverages the Mixtral model to achieve competitive performance on math reasoning benchmarks using open-source LLMs.
paper_ai_keywords: Large language models LLMs MetaMathQA MAmmoTH GPT-4 Mixtral math instruction tuning GSM8K MATH OpenMath-CodeLlama-70B code-interpreter solutions