OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training
Continual pre-training is widely used to adapt LLMs to target languages and domains, yet the mixture ratio of training data remains a sensitive hyperparameter that is expensive to tune: they must be fixed before training begins, and a suboptimal choice can waste weeks of compute. In this work, we propose OptiMer, which decouples ratio selection from training: we train one CPT model per dataset, extract each model's distribution vector, which represents the parameter shift induced by that dataset, and search for optimal composition weights post-hoc via Bayesian optimization. Experiments on Gemma 3 27B across languages (Japanese, Chinese) and domains (Math, Code) show that OptiMer consisten...
| Authors | Haiyue Song, Masao Utiyama |
|---|---|
| Primary category | cs.CL |
| Published | 30 March 2026 |
| Last updated at source | 4 April 2026 |
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