BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning

Human infants rapidly develop visual reasoning skills from minimal input, suggesting that developmentally inspired pretraining could significantly enhance the efficiency of vision-language models (VLMs). Although recent efforts have leveraged infant-inspired datasets like SAYCam, existing evaluation benchmarks remain misaligned--they are either too simplistic, narrowly scoped, or tailored for large-scale pretrained models. Additionally, training exclusively on infant data overlooks the broader, diverse input from which infants naturally learn. To address these limitations, we propose BabyVLM, a novel framework comprising comprehensive in-domain evaluation benchmarks and a synthetic training dataset created via child-directed transformations of existing datasets. We demonstrate that VLMs trained with our synthetic dataset achieve superior performance on BabyVLM tasks compared to models trained solely on SAYCam or general-purpose data of the SAYCam size. BabyVLM thus provides a robust, developmentally aligned evaluation tool and illustrates how compact models trained on carefully curated data can generalize effectively, opening pathways toward data-efficient vision-language learning paradigms.

@article{wang2025_2504.09426,
  title={ BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning },
  author={ Shengao Wang and Arjun Chandra and Aoming Liu and Venkatesh Saligrama and Boqing Gong },
  journal={arXiv preprint arXiv:2504.09426},
  year={ 2025 }
}