Meta's Byte Latent Transformer (BLT): Outperforming Tokenization-Based LLMs
2024-12-14
Meta AI researchers introduced the Byte Latent Transformer (BLT), a novel large language model architecture that processes bytes directly, rather than tokens. BLT dynamically allocates computational resources based on byte entropy, resulting in significant improvements in inference efficiency and robustness compared to tokenization-based models. Scaling experiments up to 8 billion parameters and 4 terabytes of training data demonstrate BLT's ability to match the performance of token-based LLMs while offering enhanced reasoning capabilities and handling of long-tail data. This research showcases the feasibility of training large-scale models directly on raw bytes without a fixed vocabulary.
Read more