Byte Latent Transformer (BLT). When byte-based models surpass the limits of tokenization

The episode introduces the Byte Latent Transformer (BLT), a new language model that processes the raw bytes of text rather than relying on tokenization. Unlike traditional models, the BLT dynamically creates byte “patches,” allocating computational resources according to the complexity of the input. This approach proves more efficient, reducing FLOPS by up to 50% compared to models like LLaMA 3, while maintaining or even surpassing performance across various tasks. The research highlights the BLT’s superiority in terms of scalability, robustness, and its ability to handle non-standard data, opening new perspectives for the development of more efficient and adaptable language models.