Apple Research: Mixing High-Resource Language Data Beats Hyperparameter Tuning in Data-Constrained Pretraining
Apple ML Research · rss · 2026-10-13
Apple ML Research published "Mix, Don't Tune," a paper on language model pretraining in data-constrained regimes, where training data must be repeated many times and generalization degrades. The authors compare two remedies: aggressive hyperparameter tuning (e.g., high weight decay, which regularizes by shrinking weights and restricting network capacity) versus mixing in data from a high-resource auxiliary language with a tunable ratio to diversify the training signal. Their finding: bilingual data mixing outperforms hyperparameter search in these settings, offering a more practical recipe for low-resource language pretraining.
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