Paper: Compute Optimal Tokenization - Model Parameters Scale with Bytes, Not Tokens
stochasticchasm · x · 2026-08-28
This paper investigates how the information granularity of tokens, controlled by the compression rate, affects scaling laws. The authors trained 988 latent tokenized models (BLT) and found that:
- In compute-optimal configurations, model parameter counts scale proportionally to data size measured in bytes, not tokens.
- The optimal compression rate differs from BPE and decreases with increased compute budget.
These findings generalize to latent and subword tokenization, as well as non-English languages.
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