The BabyLM Challenge is a research initiative and workshop focused on sample-efficient language model pretraining using developmentally plausible, human-scale data budgets. It provides detoxified datasets and an open-source evaluation pipeline for researchers to develop novel techniques. The challenge aims to democratize pretraining research and advance cognitive modeling of human language acquisition.