The research entitled "Byte Latent Transformer: Patches Scale Better Than Tokens," by Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, and Srinivasan Iyer, developed at FAIR (Meta), the Paul G. Allen School of Computer Science & Engineering at the University of Washington, and the University of Chicago, introduces a language learning model that overcomes the limitations associated with fixed tokenization. The goal is to show how a byte-based approach can maintain or improve performance with greater computational efficiency.
Byte Latent Transformer, context and architecture
Understanding how best to handle textual input data has long been a historical challenge in the field of language models. The use of tokenization schemes based on static vocabularies has for a long time favored the adoption of well-known models such as ChatGPT or LLaMA, where each token was derived from a set of fixed sub-lexical units. This approach became necessary to contain computational costs, since operating directly on native byte streams, without any predefined segmentation, was considered too burdensome at large scale. Traditional architectures, in fact, relying on static tokenizers, carried constraints related to the lack of information at the most elementary level, the byte, making it difficult to handle languages poorly covered by vocabularies, noisy texts, or multimodal content. The initiative behind the Byte Latent Transformer (BLT) starts directly from raw bytes and dynamically creates groups, called patches, within which computational resources are allocated proportionally to the informational complexity. No lists or schematic subsections are used; instead, a discursive strategy is adopted to describe the key points. The central idea, enabled by this architecture, is to examine the byte flow and identify high-entropy regions—segments where predicting the next byte is uncertain—and devote more substantial computational power to them. Conversely, where the sequence is more easily predictable, larger patches are created, reducing the number of high-cost global model passes.
This system is based on the integration of three components. On the one hand, there is a lightweight local model that encodes the input bytes; on the other, a large global transformer that operates on the produced patches; and finally, a local decoder that works backward from the global representations to the original bytes. In this way, the original byte-level information is never truly abandoned, since there is no fixed vocabulary as in BPE-based token models, but rather a dynamic and adaptive mapping. Compared to preexisting architectures, this ensures access to the internal structure of words, allowing for a level of understanding more deeply rooted in their constituent characters. The use of n-gram hash embeddings for bytes enriches the representation, providing the model with a composite view that balances fine granularity and extended context.
The tests presented in the research are not limited to simple theoretical comparisons. The researchers analyzed in detail the behavior across a broad spectrum of scales, training models up to 8 billion parameters with 4 trillion bytes of training data. This size is significant, as traditionally models adopting predefined tokens—such as LLaMA 3—achieve excellent performance but incur ever-increasing costs in maintaining an extensive vocabulary. In the case of the BLT, medium-sized patches of about 6 or 8 bytes are used, noting that with larger patches, not only are the global transformer steps reduced during inference, but computation management becomes more efficient. The research shows that, for the same inference cost, the Byte Latent Transformer achieves comparable, if not superior, quality to well-known token-based models. Both large-scale datasets and complex tasks such as common sense reasoning, question answering, and even code generation are considered. Particularly interesting is the comparison in terms of FLOPs, a unit of measurement for computational cost: the BLT can achieve the same performance levels as LLaMA 3, reducing FLOPs by up to 50% at the same model and training data size. This means an advantage in terms of efficiency.
The architecture leverages various techniques, such as cross-attention between the global and local levels, as well as hashed n-gram embeddings that capture linguistic patterns at multiple levels. By comparing different approaches, the research shows that the BLT surpasses models like MegaByte in terms of scaling and performance, establishing a common ground for building new experiments. In terms of robustness, the Byte Latent Transformer appears to make the model less vulnerable to textual distortions, also improving performance on translations for low-resource languages and on tasks involving orthographic manipulation.
Emerging results
In the study, the results indicate a significant step toward eliminating traditional tokenization, demonstrating that a vocabulary-free architecture can achieve performance parity with the most advanced models. The BLT offers the possibility of markedly reducing inference costs, gaining efficiency while maintaining accuracy levels. In direct comparisons, for example with LLaMA 2 and LLaMA 3, the research shows that improvement curves at equal FLOPs are comparable, if not better, when the byte-patch architecture is employed. This means that, instead of considering expanding the token-based vocabulary to reduce the number of steps—an approach that would increase the model’s final size and thus costs—the BLT opens the door to more flexible scaling. As the model scales up, the ability to increase both the size of the global model and the patch size makes it possible to maintain the same inference budget while still achieving progressive improvements.
A crucial aspect is the evaluation of metrics that are independent of tokenization. In the past, the evaluation of language model performance was based on perplexity calculated from a token vocabulary. In the case of the BLT, the research adopts Bits-Per-Byte (BPB), a universal measure as it is independent of tokenization. As the model size grows, using an average patch of 6 or 8 bytes, the Byte Latent Transformer surpasses in efficiency models with fixed tokens, controlling FLOPs and dynamically allocating computational resources to the most difficult steps.
When evaluating tasks such as ARC-E, ARC-C, HellaSwag, PIQA, MMLU, MBPP, and HumanEval, the research shows that it is possible to achieve high-level average performance without depending on token segmentation. In some cases, comparable accuracy is achieved, and in others, improvements in robustness are noted for tests characterized by noise or textual manipulations. It is demonstrated that, given equal parameters and FLOPs, consistent results and reasoning quality comparable to the best established BPE-based pipelines are attainable. Perhaps the most interesting aspect is that by moving away from predefined tokens, one paradoxically reduces the complexities introduced by segmentation heuristics and the costs of adapting to new domains or languages. Moreover, the BLT demonstrates a better ability to handle so-called long-tail data—those less common portions of text—as well as multilingual inputs not optimized for a given vocabulary, thanks to the total absence of biases induced by tokenization.
Analyses confirm that a 50% reduction in FLOPs during inference compared to equally sized token-based models does not entail a loss in performance. This balance makes the technology particularly interesting for companies and operational realities where computational costs are a strategic factor. Additionally, the approach of increasing both the global model size and the patch size opens up new avenues for scalability, reducing the typical trade-offs between computational cost, network size, and context breadth. Ultimately, the results emphasize how a byte-based, dynamic, and flexible approach can reach and sometimes surpass the boundaries of the most established token-based architectures, providing a foundation for future research on increasingly versatile and robust models.
Conclusions
The current landscape of language modeling, dominated by architectures relying on fixed tokenization, had reached a certain maturity with cutting-edge models like ChatGPT, which can perform effectively across a wide range of tasks. However, dependence on a predefined vocabulary entails intrinsic limitations: adapting to new domains, languages, or atypical textuality remains problematic, and the need to enlarge the vocabulary to reduce the number of steps into the global model introduces increasing costs and rigidity in inference. Other solutions, such as Megabyte or SpaceByte, had already glimpsed the value of moving closer to the byte, but without fully bridging the gap with the best large-scale token-based models.
The Byte Latent Transformer fits into this line of innovation, showing an approach less constrained and more closely tied to the fundamental characteristics of text. Unlike Megabyte, which was limited to static patches, the BLT uses dynamic patches dictated by the local entropy of the textual flow, allowing computational effort to be allocated only where necessary, and enabling very long patches where the text is predictable. In this way, a system is obtained that does not sacrifice quality; rather, it achieves it at lower costs, offering more agile scalability and greater resilience to noise.
From an entrepreneurial and managerial perspective, this technology should be interpreted as an opportunity to optimize hardware and operational resources. If token-based models often require substantial costs for customization, here the intrinsic versatility reduces the burden of adapting to non-canonical data, opening new markets and industrial applications involving non-standard linguistic contexts. The most forward-looking actors will recognize in the Byte Latent Transformer a model capable of handling unforeseen situations without resorting to extensive vocabularies or costly restructuring of the pipeline. It is not a matter of immediately replacing existing solutions, but of understanding that the future of language models can move onto a more elementary plane, where the boundaries between word and subword give way to byte-level granularity and the ability to reshape text representation without constraints.
The strategic consequences are clear: developers of linguistic solutions can avoid continuously chasing new tokenizers and extreme vocabulary-side optimizations, focusing instead on making computational allocation more efficient. The Byte Latent Transformer demonstrates that an alternative path exists, one that could lead to models better able to organically learn the structure and regularities of text from the elemental level. Such an approach could, with the evolution of even more accurate patching techniques, overcome barriers considered consolidated today, such as dependence on segmentation heuristics, thus gaining flexibility. This reflection, far from being an enthusiastic endorsement, rather suggests a rebalancing of priorities: instead of optimizing the tokenizer, why not rethink the very basis of textual input? Through this shift in perspective, the BLT teaches that focusing on the byte can lead to a more harmonious balance between cost, efficiency, and adaptability, opening a less rigid path that is more consistent with the variety of data that companies will increasingly have to interpret.
Source: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/
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