ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer
Modern language models still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often leads to brittle and counterintuitive behaviors even in otherwise strong reasoning models. We introduce \textbf{ByteFlow Net}, a new hierarchical architecture that removes tokenizers entirely and instead enables models to learn their own segmentation of raw byte streams into semantically meaningful units. ByteFlow Net performs compression-driven segmentation based on the coding rate of latent representations, yielding adaptive boundaries \emph{while preserving a static computation graph via Top-$K$ selection}. U...
| Authors | Chunyuan Deng, Sanket Lokegaonkar, Colin Lockard et al. |
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| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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