LLM Midtraining Guide: Data Mixing and Overfitting Prevention
cwolferesearch · x · 2026-08-19
Key dimensions for tuning LLM midtraining (CPT):
- Data mixture: Emphasize high-quality domain-specific data (math, code, science) while retaining general data. Optimal mix requires empirical small-scale ablation.
- Training duration: More isn't better. Gains saturate quickly; excessive specialized training causes diminishing returns, forgetting, or overspecialization (mitigated by proper data tuning). Token budget should be treated as a hyperparameter.
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