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arXiv · 2605.19762

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code

Yuze Zhao·Junpeng Fang·Lu Yu·Zhenya Huang·Kai Zhang·Qing Cui·Qi Liu·Jun Zhou·Enhong Chen
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Abstract

Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the claim that code improves reasoning through controlled pretraining experiments on a 10T-token corpus with fine-grained domain separation. Our findings are threefold. First, when code is restricted to standalone executable programs and Code-NL data are controlled for, code substantially improves programming ability but does not act as a general reasoning enhancer; instead, it competes with knowledge-intensive tasks, especially complex mathematical reasoning. Second, the reasoning gains often attributed to code are better explained by cross-domain structured reasoning traces, such as code-text and math-text mixtures, rather than by executable code alone. Third, increasing the density of structured math-domain samples within a fixed math budget yields substantial gains on difficult mathematical reasoning while largely preserving programming performance, suggesting that cognitive scaffolds offer a targeted way to mitigate cross-domain trade-offs. Finally, routing analyses show that data-composition effects are reflected in expert-activation patterns, providing mechanism-level evidence for competitive and synergistic interactions across domains. Our results clarify which data characteristics transfer across capability dimensions and point to more precise data-centric optimization strategies.

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Yuze Zhao, Junpeng Fang, Lu Yu, Zhenya Huang, Kai Zhang, Qing Cui, Qi Liu, Jun Zhou, Enhong Chen. 2026-05-19. What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code. https://arxiv.org/abs/2605.19762

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