title: SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning
    Logical Reasoning and Beyond
summary: Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the
  potential of Reinforcement Learning (RL) to enhance reasoning abilities in
  Large Language Models (LLMs). While open-source replication efforts have
  primarily focused on mathematical and coding domains, methods and resources for
  developing general reasoning capabilities remain underexplored. This gap is
  partly due to the challenge of collecting diverse and verifiable reasoning data
  suitable for RL. We hypothesize that logical reasoning is critical for
  developing general reasoning capabilities, as logic forms a fundamental
  building block of reasoning. In this work, we present SynLogic, a data
  synthesis framework and dataset that generates diverse logical reasoning data
  at scale, encompassing 35 diverse logical reasoning tasks. The SynLogic
  approach enables controlled synthesis of data with adjustable difficulty and
  quantity. Importantly, all examples can be verified by simple rules, making
  them ideally suited for RL with verifiable rewards. In our experiments, we
  validate the effectiveness of RL training on the SynLogic dataset based on 7B
  and 32B models. SynLogic leads to state-of-the-art logical reasoning
  performance among open-source datasets, surpassing DeepSeek-R1-Distill-Qwen-32B
  by 6 points on BBEH. Furthermore, mixing SynLogic data with mathematical and
  coding tasks improves the training efficiency of these domains and
  significantly enhances reasoning generalization. Notably, our mixed training
  model outperforms DeepSeek-R1-Zero-Qwen-32B across multiple benchmarks. These
  findings position SynLogic as a valuable resource for advancing the broader
  reasoning capabilities of LLMs. We open-source both the data synthesis pipeline
  and the SynLogic dataset at https://github.com/MiniMax-AI/SynLogic.
paper_authors._id: 683686a4bec1d6dbb3d8728d
paper_authors.name: Junteng Liu
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paper_authors.name: Zhuo Jiang
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paper_authors.name: Han Ding
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paper_authors.name: Yongyi Hu
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paper_authors.name: Chi Zhang
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paper_authors.name: Yiqi Shi
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paper_authors.name: Shitong Weng
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paper_authors.name: Aili Chen
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paper_authors.fullname: Ellie Chen
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paper_authors.name: Shiqi Chen
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paper_authors.fullname: Shiqi Chen
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paper_authors.name: Yunan Huang
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paper_authors.fullname: Yu-Nan Huang
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paper_authors.name: Mozhi Zhang
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paper_authors.name: Pengyu Zhao
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paper_authors.name: Junxian He
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paper_title: SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning
    Logical Reasoning and Beyond
paper_summary: Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the
  potential of Reinforcement Learning (RL) to enhance reasoning abilities in
  Large Language Models (LLMs). While open-source replication efforts have
  primarily focused on mathematical and coding domains, methods and resources for
  developing general reasoning capabilities remain underexplored. This gap is
  partly due to the challenge of collecting diverse and verifiable reasoning data
  suitable for RL. We hypothesize that logical reasoning is critical for
  developing general reasoning capabilities, as logic forms a fundamental
  building block of reasoning. In this work, we present SynLogic, a data
  synthesis framework and dataset that generates diverse logical reasoning data
  at scale, encompassing 35 diverse logical reasoning tasks. The SynLogic
  approach enables controlled synthesis of data with adjustable difficulty and
  quantity. Importantly, all examples can be verified by simple rules, making
  them ideally suited for RL with verifiable rewards. In our experiments, we
  validate the effectiveness of RL training on the SynLogic dataset based on 7B
  and 32B models. SynLogic leads to state-of-the-art logical reasoning
  performance among open-source datasets, surpassing DeepSeek-R1-Distill-Qwen-32B
  by 6 points on BBEH. Furthermore, mixing SynLogic data with mathematical and
  coding tasks improves the training efficiency of these domains and
  significantly enhances reasoning generalization. Notably, our mixed training
  model outperforms DeepSeek-R1-Zero-Qwen-32B across multiple benchmarks. These
  findings position SynLogic as a valuable resource for advancing the broader
  reasoning capabilities of LLMs. We open-source both the data synthesis pipeline
  and the SynLogic dataset at https://github.com/MiniMax-AI/SynLogic.
paper_ai_summary: SynLogic, a data synthesis framework, enhances the logical reasoning capabilities of Large Language Models through RL, achieving state-of-the-art performance and improving generalization across various domains.
paper_ai_keywords: Reinforcement Learning (RL) Large Language Models (LLMs) Logical Reasoning Data Synthesis BBEH Mixed Training DeepSeek-R1 DeepSeek-R1-Distill-Qwen-32B DeepSeek-R1-Zero-Qwen-32B