Identifies structured table data as a primary driver for scaling long-context reasoning in LLMs.
March 24, 2026
Original Paper
Probing How Scalable Table Data Enhances General Long-Context Reasoning
arXiv · 2603.21719
The Takeaway
The paper reveals that table data contains periodic non-vanishing dependencies that are uniquely effective for training long-context capabilities. By using a table-specific synthesis pipeline for RL, they achieve significant gains on both in-domain and out-of-domain long-context benchmarks.
From the 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 i