Paper
Probing How Scalable Table Data Enhances General Long-Context Reasoning
Authors
Huaibing Xie, Guoliang Zhao, Yang Liu, Shihan Dou, Siming Huang, Yanling Xiao, Shaolei Wang, Yiting Liu, Cheng Zhang, Shaofan Liu, Pluto Zhou
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.21719v1</id>\n <title>Probing How Scalable Table Data Enhances General Long-Context Reasoning</title>\n <updated>2026-03-23T09:05:46Z</updated>\n <link href='https://arxiv.org/abs/2603.21719v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.21719v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <published>2026-03-23T09:05:46Z</published>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Huaibing Xie</name>\n </author>\n <author>\n <name>Guoliang Zhao</name>\n </author>\n <author>\n <name>Yang Liu</name>\n </author>\n <author>\n <name>Shihan Dou</name>\n </author>\n <author>\n <name>Siming Huang</name>\n </author>\n <author>\n <name>Yanling Xiao</name>\n </author>\n <author>\n <name>Shaolei Wang</name>\n </author>\n <author>\n <name>Yiting Liu</name>\n </author>\n <author>\n <name>Cheng Zhang</name>\n </author>\n <author>\n <name>Shaofan Liu</name>\n </author>\n <author>\n <name>Pluto Zhou</name>\n </author>\n </entry>"
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