Scaling Hierarchical Networks

Cartesia Research introduces hierarchical networks (H-Nets), a technology designed to enable language models to learn directly from raw bytes and characters rather than relying on hand-crafted tokenizers, addressing a key limitation in current large language models. The researchers argue that developing byte-level models is essential for advancing AI progress, particularly as the field tackles more complex domains like science and robotics where data is scarce. Early results show that carefully controlled H-Nets achieve meaningful computational efficiency gains compared to standard tokenized Transformers.