Probing How Scalable Table Data Enhances General Long-Context Reasoning

• URL: http://arxiv.org/abs/2603.21719v1
• Date: Mon, 23 Mar 2026 09:05:46 GMT
• Title: Probing How Scalable Table Data Enhances General Long-Context Reasoning
• Abstract summary: We find that structured table data with periodic structures shows strong potential for long-context reasoning.<n>We propose a simple yet scalable pipeline(TableLong) for synthesizing high-quality, diverse, and verifiable structured table data.
• Score: 18.383487310920597
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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