Mix, Don’t Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
AI Summary
This article investigates language model pre-training in data-constrained regimes where models must repeatedly train on limited data. By comparing hyperparameter tuning against mixing high-resource auxiliary language data across four model scales (150M to 1.43B parameters) over 1000 runs, the authors find that data mixing consistently outperforms hyperparameter tuning. Mixing expands the training distribution and diversifies signals, yielding performance gains equivalent to 2–3x unique target data on validation loss and 2–13x on downstream tasks.
Key Takeaways
- Data mixing yields larger improvements than hyperparameter tuning, with performance gaps growing alongside model size.
- Performance boosts equal 2–3x unique target data on validation loss and 2–13x on downstream task accuracy.
- Target-language validation loss underestimates mixing's true value because it captures regularization but misses the added knowledge contribution.
Why it matters
For engineers and researchers working on low-resource language models, data mixing offers a more effective, scalable remedy to data constraints than costly hyperparameter searches.
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