Continued pretraining or fine-tuning: how to choose for your model

Continued pretraining suits cases where a base model lacks your domain's vocabulary and facts, and it learns from unlabeled text such as contracts, papers, or codebases. Fine-tuning works better when the model already knows the material but needs to follow a format, tone, or task behavior, and it is trained on labeled examples. Gururangan et al. (ACL 2020) found in-domain pretraining improved results across several domains and tasks. The article proposes a three-part test: the base model struggles with domain terms, you hold far more raw text than labeled data, and the knowledge is stable enough to bake into weights. Continued pretraining usually costs far more compute than fine-tuning, so teams should often try fine-tuning on a few thousand examples first and measure results.