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 …

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 teacher's token logits into byte logits, either approximately (Marginalize-It) or exactly (End-Of-Token). Token models lead at low compute but plateau. Byte models reach a higher ceiling, and the fitted scaling laws predict the End-Of-Token model ends up to 4% ahead of the distilled token model. The byte models also match the distilled token model with one-sixth of the training data, and a 256-entry vocabulary cuts teacher-logit storage to about a fifth. Paper: https://arxiv.org/abs/2609.12303 Chat with Paper: https://academy.dair.ai/papers/breaking-the-token-ceiling-distilling-smaller-stronger-byte-models-2609.12303

Source: DAIR.AI (X) | 2026-09-14