OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training
For practitioners of continual pre-training of LLMs, OptiMer offers a more flexible and cost-effective paradigm that replaces expensive pre-training hyperparameter tuning with post-hoc optimization.
OptiMer decouples data mixture ratio selection from training by training one CPT model per dataset, extracting distribution vectors, and optimizing composition weights post-hoc via Bayesian optimization. On Gemma 3 27B, it outperforms data mixture and model averaging baselines with 15-35x lower search cost.
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 consistently outperforms data mixture and model averaging baselines with 15-35 times lower search cost. Key findings reveal that 1) the optimized weights can be interpreted as data mixture ratios, and retraining with these ratios improves data mixture CPT, and 2) the same vector pool can be re-optimized for a given objective without any retraining, producing target-tailored models on demand. Our work establishes that data mixture ratio selection, traditionally a pre-training decision, can be reformulated as a post-hoc optimization over distribution vectors, offering a more flexible paradigm for continual pre-training.