Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books. We proposeMegaByte, a multi-scale decoder architecture that enables end-to-end differentiable modeling of sequences of over one million bytes.MegaBytesegments sequences into patches and uses alocalsubmodel within patches and aglobalmodel between patches. This enables sub-quadratic self-attention, much larger feedforward layers for the same compute, and improved parallelism during decoding—unlocking better performance at reduced cost for both training and generation.
Extensive experiments show thatMegaByteallows byte-level models to perform competitively with subword models on long context language modeling, achieve state-of-the-art density estimation on ImageNet, and model audio from raw files. Together, these results establish the viability of tokenization-free autoregressive sequence modeling at scale.