构建可扩展的符号推理数据生成工具，提升语言模型的逻辑推理能力
Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training
- 基于五类形式化领域生成可验证的符号推理数据
- 混合使用该数据后，下游推理任务性能显著提升
- 支持从预训练到强化学习的全流程验证与训练
在可验证的符号数据上训练是拓展语言模型推理能力的有效途径，但现有程序化生成器多依赖固定谜题或模板，难以实现大规模分布多样性。我们提出 Reasoning Core，一个可扩展的数据生成套件，能跨五大核心形式领域生成符号推理数据：随机域上的PDDL规划、含等价关系的一阶逻辑、上下文无关文法解析与生成、随机贝叶斯网络上的因果推理，以及方程组系统。每项任务均配备外部求解器进行严格验证，并支持连续难度控制以实现课程设计。数据可选包含求解器生成的推理轨迹，支持从预训练初期开始的监督训练；同一接口也可提供强化学习所需的可验证奖励函数。实验表明，将 Reasoning Core 数据融入预训练可显著提升下游推理表现，同时保持或小幅改善语言建模质量。零样本评估显示，这些任务已对 GPT-5 等前沿模型构成挑战。代码与数据已开源，采用 MIT 许可证。
原文摘要 · Abstract (English)
Training on verifiable symbolic data is a promising way to expand the reasoning frontier of language models beyond what standard pre-training corpora provide. Yet existing procedural generators often rely on fixed puzzles or templates and do not deliver the distributional breadth needed at scale. We introduce Reasoning Core, a scalable suite that procedurally generates verifiable symbolic reasoning data across core formal domains: PDDL planning over randomized domains, first-order logic with equality, context-free grammar parsing and generation, causal reasoning over random Bayesian networks, and systems of equations. Each task is paired with an external solver for rigorous verification and admits continuous difficulty control for curriculum design. Examples can optionally include solver-derived reasoning traces, enabling supervised training from the earliest pre-training stages, and the same interface provides verifiable reward functions for reinforcement learning. Our experiments show that mixing Reasoning Core data into pre-training improves downstream reasoning while preserving, or slightly improving, language modeling quality. Zero-shot evaluations confirm these tasks challenge frontier models such as GPT-5. The code and data are publicly available under the MIT license.
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