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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint reports an exploratory computational study evaluating whether pre-training on non-language symbolic data (music, probabilistic grammars, cellular automata) can serve as a structural prior to improve language model training efficiency. While several non-language data types reduced next-token-prediction loss and yielded smaller weight shifts compared to random initialization, these gains did not reliably translate into better performance on downstream linguistic benchmarks, and non-language transfer was less efficient than simply using additional language data. The work is mechanistic and hypothesis-generating rather than practice-changing.
Exploratory computational experiment. Artificial neural network language models trained for multilingual language modeling; no human participants or biological systems.. Intervention: Pre-training on non-language symbolic data (music, probabilistic grammars, cellular automata) as weight initialization for language models.. Compared with: Random initialization and transfer from additional language data..
Several symbolic data types—notably music, probabilistic grammars, and cellular automata—yield lower language-modeling loss than random initialization. Lower loss coincides with smaller weight shifts during subsequent language training, suggesting favorable parameter-space positioning. Lower loss does not translate consistently into better downstream linguistic performance.
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An uncontrolled, exploratory computational study investigating a mechanistic hypothesis about weight initialization; results on a surrogate endpoint (loss) do not translate to downstream performance, limiting actionability.
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Efficient language learning requires methods to reduce the reliance on large data and computational resources. We investigate structural transfer: First training models on non-language data to induce useful priors for natural language. This approach is a form of weight initialization for multilingual language modeling. We evaluate transfer via next-token-prediction loss, weight shifts in the model, and downstream linguistic benchmarks. Several symbolic data types - notably music, probabilistic grammars, and cellular automata - yield lower language-modeling loss than random initialization. These gains coincide with smaller weight shifts during subsequent language training, suggesting that structural transfer positions models in a more favorable region of the parameter space. However, a lower loss does not translate consistently into better downstream linguistic performance, and transfer from non-language data is less efficient than additional language data. We conclude that non-language data can serve as a partial substitute for language data for the training objective of next-token prediction but does not reliably support broader linguistic generalization.
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