@MindsAI_Jack: Step in the right direction. As far as I know, pioneered at Google originally with ByT5 that was more resilient against…
Summary
A research paper from Meta demonstrates that byte-level language models, initially inferior to token-based models, can outperform them as computational resources increase, shown through distilled 1B models trained on up to 1 trillion bytes.
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Cached at: 09/15/26, 07:41 AM
Step in the right direction. As far as I know, pioneered at Google originally with ByT5 that was more resilient against noise (misspellings and so forth).
elvis (@omarsar0): Banger paper from Meta.
This work shows that byte-level models start out behind token models and then pass them as compute grows.
They show this for distilled 1B models trained on up to 1 trillion bytes.
To distill a byte student from a token teacher, they convert the
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