efficient-pretraining

HRM-Text introduces a Hierarchical Recurrent Model that decouples computation into slow and fast layers, enabling efficient pretraining from scratch on only 40 billion tokens and a $1,500 budget, achieving competitive performance with larger models.

HRM-Text is a 1B parameter text generation model that uses a brain-inspired hierarchical recurrent architecture to achieve efficient pretraining with only 40B tokens and ~$1000, enabling accessible foundation model training with dramatically reduced compute and data requirements.