title: Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis
    from Scratch
summary: The availability of high-quality data is one of the most important factors in
  improving the reasoning capability of LLMs. Existing works have demonstrated
  the effectiveness of creating more instruction data from seed questions or
  knowledge bases. Recent research indicates that continually scaling up data
  synthesis from strong models (e.g., GPT-4) can further elicit reasoning
  performance. Though promising, the open-sourced community still lacks
  high-quality data at scale and scalable data synthesis methods with affordable
  costs. To address this, we introduce ScaleQuest, a scalable and novel data
  synthesis method that utilizes "small-size" (e.g., 7B) open-source models to
  generate questions from scratch without the need for seed data with complex
  augmentation constraints. With the efficient ScaleQuest, we automatically
  constructed a mathematical reasoning dataset consisting of 1 million
  problem-solution pairs, which are more effective than existing open-sourced
  datasets. It can universally increase the performance of mainstream open-source
  models (i.e., Mistral, Llama3, DeepSeekMath, and Qwen2-Math) by achieving 29.2%
  to 46.4% gains on MATH. Notably, simply fine-tuning the Qwen2-Math-7B-Base
  model with our dataset can even surpass Qwen2-Math-7B-Instruct, a strong and
  well-aligned model on closed-source data, and proprietary models such as
  GPT-4-Turbo and Claude-3.5 Sonnet.
paper_authors._id: 671b0c864e0ac814c30b16f1
paper_authors.name: Yuyang Ding
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-25T09:29:45.039Z
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paper_authors.fullname: Ding
paper_authors.type: user
paper_authors.user: dyyyyyyyy
paper_authors._id: 671b0c864e0ac814c30b16f2
paper_authors.name: Xinyu Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-25T11:25:46.041Z
paper_authors._id: 6380ff85f496d57325c2ac2b
paper_authors.avatarUrl: /avatars/0b9eb9c8a96d5ffd905f9ddeee78e6f3.svg
paper_authors.fullname: Xinyu Shi
paper_authors.type: user
paper_authors.user: XinyuShi
paper_authors._id: 671b0c864e0ac814c30b16f3
paper_authors.name: Xiaobo Liang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:54:48.587Z
paper_authors._id: 64116770230ce11b1be7f3b2
paper_authors.avatarUrl: /avatars/d554406a121666c3e16bc3321415ccb1.svg
paper_authors.fullname: Xiaobo Liang
paper_authors.type: user
paper_authors.user: dropreg
paper_authors._id: 671b0c864e0ac814c30b16f4
paper_authors.name: Juntao Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-25T11:26:05.777Z
paper_authors._id: 6670e285b0c03c4e9d6e0985
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uCZHm4gKSHZ2b0hpHWgZv.jpeg
paper_authors.fullname: Juntao Li
paper_authors.type: user
paper_authors.user: douvleplus
paper_authors._id: 671b0c864e0ac814c30b16f5
paper_authors.name: Qiaoming Zhu
paper_authors._id: 671b0c864e0ac814c30b16f6
paper_authors.name: Min Zhang
paper_title: Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis
    from Scratch
paper_summary: The availability of high-quality data is one of the most important factors in
  improving the reasoning capability of LLMs. Existing works have demonstrated
  the effectiveness of creating more instruction data from seed questions or
  knowledge bases. Recent research indicates that continually scaling up data
  synthesis from strong models (e.g., GPT-4) can further elicit reasoning
  performance. Though promising, the open-sourced community still lacks
  high-quality data at scale and scalable data synthesis methods with affordable
  costs. To address this, we introduce ScaleQuest, a scalable and novel data
  synthesis method that utilizes "small-size" (e.g., 7B) open-source models to
  generate questions from scratch without the need for seed data with complex
  augmentation constraints. With the efficient ScaleQuest, we automatically
  constructed a mathematical reasoning dataset consisting of 1 million
  problem-solution pairs, which are more effective than existing open-sourced
  datasets. It can universally increase the performance of mainstream open-source
  models (i.e., Mistral, Llama3, DeepSeekMath, and Qwen2-Math) by achieving 29.2%
  to 46.4% gains on MATH. Notably, simply fine-tuning the Qwen2-Math-7B-Base
  model with our dataset can even surpass Qwen2-Math-7B-Instruct, a strong and
  well-aligned model on closed-source data, and proprietary models such as
  GPT-4-Turbo and Claude-3.5 Sonnet.
paper_ai_keywords: LLMs instruction data data synthesis GPT-4 reasoning performance seed questions knowledge data scale scalable data synthesis methods ScaleQuest small-size open-source models mathematical reasoning dataset problem-solution pairs text reasoning Mistral Llama3 DeepSeekMath Qwen2-Math MATH Qwen2-Math-7B-Base Qwen2-Math-7B-Inference GPT-4-Turbo Claude-3 Sonnet