Meta’s recently open-sourced Byte Latent Transformer (BLT) offers a learned dynamic byte-processing scheme, improving performance while reducing inference FLOPS compared to traditional token-based models.
BLT's innovative approach addresses the 'strawberry problem' through the dynamic grouping of bytes into patches, leveraging a small model to assess entropy and enhance robustness to noisy inputs.
By interacting directly with bytes, BLT proves beneficial in managing the long-tail of data, which allows for greater robustness and a deeper understanding of language nuances compared to tokenization.
The architecture of BLT provides a scalable framework that not only matches the efficacy of Llama 3 with improved efficiency but also opens up new possibilities for model and patch size scaling.
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