Probing How Scalable Table Data Enhances General Long-Context Reasoning
The paper tackles the challenge of enhancing long-context reasoning in Large Language Models (LLMs), a critical capability as real-world tasks grow more complex. It proposes structured table data as a solution, mathematically demonstrating via mutual information analysis that tables possess periodic non-vanishing dependencies—unlike natural language which decays polynomially—making them ideal for training long-context reasoning. The authors present TableLong, a scalable pipeline for synthesizing diverse, verifiable table data for reinforcement learning, showing significant performance gains across benchmarks.
The paper makes a compelling theoretical and empirical case for using structured table data to enhance long-context reasoning. The mutual information framework provides a rigorous foundation distinguishing tabular data from natural language, while the TableLong pipeline demonstrates practical applicability. However, the theoretical analysis relies on idealized assumptions that may not hold for real-world tables, and the claimed out-of-domain generalization to math and code domains may conflate general reasoning improvements with specific long-context capability transfer.
The mutual information analysis (Section 2) is novel and rigorous, establishing that $\bar{I}_{table}(km) = I^{same} > 0$ for periodic lags, contrasting sharply with natural language's $I_{text}(d) \sim C \cdot d^{-\alpha}$ decay. The decomposition experiments (Section 4.3) robustly validate mechanisms: structure alone provides +1.67% improvement, confirming the theoretical insight that periodic non-vanishing dependencies drive long-context capability. The empirical evaluation spans seven diverse long-context benchmarks showing consistent gains (+8.24% on average), with systematic ablations on length, multi-hop complexity, and grounding.
The theoretical framework assumes Column Semantic Consistency (A1) and Column Distribution Distinctiveness (A2) which may not hold for messy real-world tables, limiting the theory's applicability to idealized cases. The claim that table data generalizes to OOD math and code domains (Section 4.4) conflates general reasoning improvements with specific long-context capability transfer—these benchmarks may not adequately test long-range retrieval. The consistency-based filtration (Section 3.4) relies on the model's own pass rate $P$, potentially reinforcing existing capabilities rather than inducing new long-context reasoning skills. Additionally, the comparison against natural language assumes a specific power-law decay that, while empirically observed, may not be universal across all text corpora.
The evidence strongly supports the superiority of table data over natural language for long-context training, but lacks direct comparison against other structured formats (e.g., trees, knowledge graphs) that might exhibit similar non-vanishing dependency properties. The comparison to Qwen-Long-L1 (Table 1) shows favorable results, though differences in training methodology and data volume confound direct attribution. While the ablation studies effectively isolate structure from semantics (Section 4.3.1), they do not isolate SQL-specific reasoning from general tabular understanding, leaving open whether the gains derive from the data structure or the query complexity.
The paper provides comprehensive training details in Appendix B (64 H20 GPUs, GRPO algorithm, learning rate $2 \times 10^{-6}$, clip ratios $\epsilon_{high}=0.28$, batch sizes) and benchmark specifications in Appendix C, which facilitates reproduction. However, the complete data generation code and the final filtered dataset are not publicly released, which would block exact reproduction. The reliance on gpt-oss-120B as an outcome-based judge for verification (Appendix B) may limit reproducibility for researchers without access to this specific model, though the paper notes this is only for reward modeling.
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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