SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning
  Logical Reasoning and Beyond
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.
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Junteng Liu
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2025-05-28T12:15:12.066Z
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Junteng Liu
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Yuanxiang Fan
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2025-05-28T12:15:18.907Z
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Yuanxiang Fan
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Zhuo Jiang
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Han Ding
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Yongyi Hu
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Chi Zhang
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Yiqi Shi
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Shitong Weng
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Aili Chen
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2025-05-29T20:18:50.609Z
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Ellie Chen
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Shiqi Chen
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Shiqi Chen
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ShiqiChen
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Yunan Huang
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2025-05-28T12:15:45.974Z
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Yu-Nan Huang
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Mozhi Zhang
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2025-05-28T12:15:40.524Z
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Mozhi Zhang
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Pengyu Zhao
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Junjie Yan
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2025-05-28T12:15:34.200Z
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JunjieYan
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Junxian He
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2025-05-28T12:15:27.539Z
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Junxian He
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jxhe
SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning
  Logical Reasoning and Beyond
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.
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.
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