Dynamic Multi-Byte Prediction With Hierarchical Language Models

Abraham Owodunni, okocha chibuzor joseph, Christan Grant, Tomasz Limisiewicz, Sachin Kumar

Dynamic Multi-Byte Prediction With Hierarchical Language Models: 19 upvotes on Hugging Face Daily Papers, #12 of 33 papers on 2026-08-19. Day-by-day upvote history.

Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucial innovations. First, we introduce a variable-length prediction window that aligns with the latent tokens, or segments, of a hierarchical LM. Second, we implement a novel attention-masking scheme that enables parallel byte prediction without violating causality. We show that multi-byte prediction strikes a Pareto-optimal trade-off across multiple generative tasks, instruction following, question answering, summarization, and machine translation, achieving the best trade-off between performance and inference throughput.

Paper page on Hugging Face · arXiv

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