Byte Latent Transformer: Patches Scale Better Than Tokens
AI Papers Podcast Daily · AIPPD
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Show Notes
BLT (Byte Latent Transformer) is a new type of large language model (LLM) that processes text directly at the byte level, unlike traditional LLMs that rely on pre-processing text into tokens. This novel approach, based on dynamic patching, groups bytes into larger units called patches, whose size is determined by the predictability of the following byte, as calculated by a separate byte-level language model. This allows BLT to dynamically allocate computational resources to areas of higher complexity, leading to improved efficiency. The BLT architecture consists of three main modules: a Local Encoder to convert bytes into patches, a Latent Transformer to process these patches, and a Local Decoder to transform patches back to bytes. Extensive experimentation has shown that BLT models achieve performance comparable to, or even exceeding, token-based models like Llama 3, while demonstrating greater efficiency and robustness, especially when handling noisy data and performing character-level tasks. Significantly, BLT showcases superior scaling capabilities, allowing simultaneous increases in model and patch size for a fixed computational budget, suggesting a promising future for byte-level language models.