Probing How Scalable Table Data Enhances General Long-Context Reasoning

2026-03-23

AI Summary

This study investigates which data types effectively enhance the long-context reasoning capabilities of large language models. It reveals, for the first time, that periodic and non-decaying dependencies inherent in structured tabular data play a critical role in long-range reasoning. Building on this insight, the work proposes a verifiable, diverse, and scalable method for synthesizing tabular data, integrated with a mutual information–based dependency analysis and a reinforcement learning training framework. The proposed approach achieves an average improvement of 8.24% across multiple long-context benchmarks and demonstrates a consistent gain of 8.06% on out-of-domain tasks, significantly boosting the model’s long-context reasoning performance.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• long-context reasoning
• structured table data
• Large Language Models
• data types
• reasoning capability

Innovation

• structured table data
• long-context reasoning
• mutual information
• scaling law
• reinforcement learning