As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types are effective for long-context reasoning and why. We find that structured table data with periodic structures shows strong potential for long-context reasoning. Motivated by this observation, we mathematically analyze tabular dependency structures using mutual information, revealing periodic non-vanishing dependencies in table data. Furthermore, we systematically analyze the capabilities of structured table data, conduct relevant scaling experiments, and validate its underlying mechanisms for enhancing long-context reasoning, yielding several meaningful insights. Leveraging these insights, we propose a simple yet scalable pipeline(TableLong) for synthesizing high-quality, diverse, and verifiable structured table data to boost long-context reasoning via RL. Extensive experimental results demonstrate that table data significantly enhances the long-context reasoning capability of LLMs across multiple long-context benchmarks (+8.24\% on average), and even improves performance on out-of-domain benchmarks (+8.06\% on average). We hope that our insights provide practical guidance for effective post-training data to enhance long-context reasoning in LLMs.
@article{arxiv.2603.21719,
title = {Probing How Scalable Table Data Enhances General Long-Context Reasoning},
author = {Huaibing Xie and Guoliang Zhao and Yang Liu and Shihan Dou and Siming Huang and Yanling Xiao and Shaolei Wang and Yiting Liu and Cheng Zhang and Shaofan Liu and Pluto Zhou},
journal= {arXiv preprint arXiv:2603.21719},
year = {2026}
}