Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)
1,389#tokenization #llm #meta
This paper does away with tokenization and creates an LLM architecture that operates on dynamically sized "patches" instead of tokens. By controlling the patch size, they gain a level of control over the tradeoff between model size and FLOPs and use that to achieve more favorable scaling behavior than classically tokenized LLMs.
Paper: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/
Code: https://github.com/facebookresearch/blt
Abstract:
We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented dynamically based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it. We present the first flop controlled scaling study of byte-level models up to 8B parameters with 4T training bytes. Our results demonstrate the feasibility of scaling models trained on raw bytes without a fixed-vocabulary. Both training and inference efficiency improve due to dynamically selecting long patches when data is predictable, along with qualitative improvements on reasoning and long tail generalization. Overall, for fixed inference costs, BLT shows significantly better scaling than tokenization-based models, by simultaneously growing both patch and model size.
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