A Comprehensive Guide to Byte Latent Transformer Architecture

We all know by now that large language model (LLM) are used almost everywhere and are quite powerful. However these LLMs are heavily relied on the process of tokenization to process text efficiently. A technique used to process large sentences or phrases by dividing them into smaller tokens which can be words, subwords or characters, these tokens are then passed to the machine learning models so that they can be processed efficiently. However, this traditional approach comes with hidden costs—biases in token compression, sensitivity to noise, and challenges in multilingual processing. What if we could eliminate tokenization altogether and train models directly on raw bytes without sacrificing efficiency or performance?

In this article we will dive into the paper Byte Latent Transformer: Patches Scale Better Than Tokens. This paper introduces the novel concept of a tokenizer free architecture or byte-level LLM architecture (BLT).

Unlike traditional models based on a fixed vocabulary of tokens, the Byte Latent Transformer dynamically groups bytes into latent patches, allowing the model to allocate computational resources where they matter most. This adaptive approach improves efficiency and enhances robustness, making BLT models better at handling noisy inputs, understanding character-level structures, and processing diverse languages more efficiently.
BLT processes raw text by grouping bytes into flexible chunks called patches rather than using fixed tokens. These patches are dynamically sized, meaning the model creates larger patches when the text is predictable (saving compute) and smaller, more detailed patches when the text is complex (allocating more compute). This smart allocation helps BLT train efficiently, even on massive datasets (8 billion parameters, 4 trillion bytes of data). Unlike traditional models that always use the same-sized tokens, BLT adapts in real-time, making training and inference faster and more efficient.

The concepts mentioned below will help you understand Byte Latent Transformer better.

Tokenization in Language Models

• Traditional LLMs (e.g., GPT, Llama) use subword tokenization methods like Byte Pair Encoding (BPE) or WordPiece to break text into tokens before training.
• Tokens are predefined chunks of words or characters that the model learns from.

Transformer Architecture Basics

• The Transformer is the backbone of most modern LLMs. Key components include:
• Self-attention (how models focus on different parts of input data).
• Feed-forward layers (used for learning patterns in data).

Entropy in Language Models

• Entropy measures uncertainty in predictions.
• High entropy means the model is uncertain about the next byte/token, while low entropy means high confidence.
• Used in BLT for dynamically deciding patch boundaries.

In simpler words, Byte Latent Transformers (BLTs) is the new approach to process languages that eliminates the need for predefined tokenization. Traditional AI models, like those used in Llama 2 and 3, rely on tokenizers that break text into smaller units (tokens) before feeding them into the model. While this method works well, it can be limiting—especially when dealing with multiple languages or new types of data.

BLTs take a different approach by working directly with raw bytes, grouping them into “patches” instead of predefined tokens. This patch-based system allows the model to be more flexible and efficient, reducing the computational cost of processing text. Since larger patch sizes mean fewer processing steps, BLTs can scale up without dramatically increasing the training budget. This makes them particularly useful for handling large datasets and complex languages, while also improving inference speed.

Although BLTs are still being optimized, early results show they can match or even outperform traditional models at scale. As research continues, BLTs could pave the way for more efficient and universally adaptable AI models.

Let us first understand what an Entropy in the Byte Latent Transformer (BLT) refers to. The Entropy here is the level of uncertainty or information content in the byte sequences being processed.

In simple terms, it tells us how “uncertain” the next byte in a sequence is, based on the model’s prediction.

• If the entropy is high, the model is uncertain about what the next byte will be.
• If the entropy is low, the model is more confident about the next byte.

Entropy measures how much randomness or unpredictability is present in a sequence of bytes. In BLT, the entropy of a byte sequence impacts:

• Compression Efficiency: Higher entropy means more unique patterns, making it harder to compress, while lower entropy suggests more predictable structures that can be represented efficiently.
• Model Complexity Control: BLT adapts its computation based on entropy, determining when to invoke the Latent Global Transformer to reduce unnecessary processing.
• Representation Learning: By capturing patterns in byte sequences, BLT learns efficient representations that balance complexity and expressiveness.

Entropy patching is a method used to decide where to split byte sequences into patches based on the uncertainty (entropy) of the next byte prediction. This approach helps dynamically determine the boundaries between patches, which are units of computation for the Byte Latent Transformer (BLT). Unlike traditional rule-based methods (such as splitting on whitespace), entropy patching leverages a data-driven approach, calculating entropy estimates to identify where the next byte prediction becomes uncertain or complex.

How is Entropy Used for Patch Boundaries?

BLT uses a small byte-level language model (LM) to estimate the entropy of each byte in the sequence. This is done for each byte xi, and it helps decide where to split the sequence into patches.

Equation for Entropy (H(xi))

The entropy H(xi) for each byte xi is calculated as follows:

The entropy calculation allows the model to adaptively determine patch boundaries based on where the data is uncertain or complex. Also, by defining patch boundaries where there’s high uncertainty (high entropy), BLT reduces unnecessary computations for predictable parts of the data.

In simple terms, entropy patching in BLT helps identify where to break a byte sequence into smaller parts (patches) by measuring the “uncertainty” in predicting the next byte. The more uncertain the next byte is, the more likely it is that a new patch boundary will be created.

Modern large language models (LLMs), including Llama 3, use something called subword tokenization. This means the model breaks down text into smaller pieces (tokens), but these pieces aren’t always full words. Instead, they can be parts of words, like syllables or even smaller fragments. The tokenizer does this by using a predefined set of pieces, or tokens, that it learned from the training data. These tokens are not dynamic; they come from a fixed list.

Patches vs. Tokens

• In contrast to tokens, patches are sequences of bytes that are grouped together dynamically during the model’s operation, not from a fixed list. This means patches don’t follow a fixed vocabulary and can vary depending on the input.
• The big difference between tokens and patches is that with tokens, the model doesn’t have direct access to the actual raw bytes (the basic units of data). But with patches, the model can directly handle the raw bytes and group them dynamically.

BLT’s Advantage Over Tokenization

• The BLT model (Byte Latent Transformer) improves upon tokenization-based models. In traditional models, when you increase the vocabulary size (more tokens), the tokens tend to get larger, which reduces the number of processing steps the model needs to take. But this also means the model needs more computing power, especially for things like the final layer that projects the model’s output.
• BLT changes this balance: it allows for better flexibility in how the data is grouped and processed, making it more efficient in some cases. For example, Llama 3 increases its token size a little but has to use a much larger embedding table (which requires more resources).

How Does BLT Decide When to Split the Data?

• When BLT is generating text, it needs to decide whether the current part of the data should be split into a new patch or not. This decision has to happen on the fly, based only on the data that has already been processed, without knowing what comes next.
• This is important because BLT works with a dynamic approach—it can’t peek ahead in the text to decide how to split it. It has to make that decision step by step, which is called incremental patching.

Why Doesn’t Tokenization Work the Same Way?

In a typical tokenization system, the model doesn’t work in an incremental way. For instance, if you look at the start of a word and try to break it down into tokens, the model might split it differently based on what comes next in the word. This means tokenization can change based on the future text, which doesn’t meet the needs of an incremental process like BLT, where the model must decide without knowing future data.

As we understood The Byte Latent Transformer (BLT) is designed to process raw bytes efficiently without relying on traditional tokenization and It does that by grouping bytes into dynamically sized patches. The BLT consists of three main components:

1. Global Transformer Model (Latent Global Transformer)
2. Local Encoder (Transforms bytes into patches)
3. Local Decoder (Converts patches back into bytes)

Each of these components plays a crucial role in making BLT an efficient and scalable model for language processing.

1. Global Transformer Model (Latent Global Transformer)

• This is the main powerhouse of the BLT architecture.
• It processes sequences of patch representations rather than individual bytes.
• Works autoregressively, meaning it predicts the next patch based on previous patches.
• Uses a block-causal attention mask, which ensures the model only attends to the current and past patches, improving efficiency.
• Since this is the most computationally expensive part, BLT intelligently decides when to invoke it, optimizing the compute cost based on input complexity.

2. Local Encoder (Converting Bytes into Patches)

• This is a smaller, lightweight transformer responsible for converting raw bytes into patch representations.
• Uses a special cross-attention mechanism to efficiently pool byte information into patches.
• Incorporates hash-based n-gram embeddings, meaning it captures patterns of multiple consecutive bytes (3 to 8 bytes) to improve understanding.
• Uses a block-causal attention mask within local regions, meaning each byte only attends to nearby bytes while forming patches.

3. Local Decoder (Converting Patches back to Bytes)

• Another small transformer, but this one works in the opposite direction of the encoder.
• Takes processed patch representations and reconstructs the original byte sequences.
• Uses a cross-attention mechanism where patch representations guide the byte-level generation.
• Ensures high fidelity in generating outputs by refining byte details within each patch.

How BLT Works Together

1. Encoding Phase:
   • The Local Encoder groups bytes into patches by looking at patterns and compressing information efficiently.
   • Hash-based n-gram embeddings help capture longer context without increasing computational cost.
2. Processing Phase:
   • The Global Transformer works on patch representations instead of raw bytes, making computation more efficient.
   • Uses adaptive patch sizing, so it spends more compute on complex text and less on predictable text.
3. Decoding Phase:
   • The Local Decoder reconstructs the original byte sequence from the processed patches using cross-attention.

Byte Latent Transformers offers several advantages over the traditional ones that mostly rely on byte pair encoding (BPE) tokenization. These benefits make BLTs a strong choice for large-scale language models. Here we will discuss a few key advantages of BLTs.

No Dependence on BPE Tokenization: Traditional transformers rely on BPE tokenization, where performance can vary depending on the tokenizer used (e.g., Llama 2 vs. Llama 3 tokenizers). In contrast, BLTs work independently of tokenization methods, making them more flexible.

More Efficient Computation: BLTs use larger patch sizes (e.g., 8 bytes) instead of smaller token-based inputs. This leads to nearly 50% savings in inference FLOPs, making them more efficient for deployment without sacrificing performance.

Reduced Computational Cost for Larger Models: BLTs grow total parameters efficiently—while increasing model size 20x from 400M to 8B, BLT’s local parameters only double. This allows BLTs to scale without exponentially increasing compute requirements, making them ideal for large-scale AI models.

Fixed-Vocabulary Tokenization: Unlike traditional models, BLTs do not suffer from the efficiency trade-offs of fixed-vocabulary tokenization.

While BLTs offer several advantages over traditional transformers, they also come with some limitations:

• BLTs currently use scaling laws designed for BPE-based transformers, which may not be optimal for their architecture. Future research is needed to develop BLT-specific scaling laws that could further improve their efficiency and performance.
• Existing deep learning libraries are highly optimized for tokenizer-based models, making it difficult to achieve the same level of efficiency with BLTs.
• BLTs require specialized implementations (such as FlexAttention) but may still not match BPE-based models in terms of wall-clock time.
• While early experiments suggest that “byte-ifying” tokenizer-based models (e.g., Llama 3) is possible, the process is not yet fully optimized.
• More research is needed to ensure that BLTs can match or surpass tokenizer-based models without requiring full retraining.

1. How does BLT differ from traditional transformers?

Traditional transformers use tokenization, where text is split into smaller units (words or subwords) before processing. BLTs, on the other hand, operate directly on byte sequences, grouping them into patches. This eliminates the need for tokenization and allows BLTs to work efficiently with any language or dataset without relying on predefined vocabularies.

2. What are the benefits of BLT over tokenization?

• Greater Flexibility: Works with any language or text format without a tokenizer.
• Improved Efficiency: Larger byte patches reduce computational overhead and improve scaling.
• Better Performance at Scale: BLTs match or outperform token-based models as they grow in size.
• Reduced Preprocessing: No need to train and fine-tune separate tokenizers for different languages.

3. Is BLT suitable for multilingual data?

Yes! Since BLTs work with raw bytes rather than language-specific tokens, they can naturally handle multiple languages, including those with complex scripts. This makes them particularly effective for multilingual AI models, eliminating the need for separate tokenization rules for each language.

4. Can BLT be integrated with existing AI models?

Yes, BLTs can be integrated with existing AI architectures, and early experiments show promising results in “byte-ifying” tokenizer-based models like Llama 3. While some optimizations are still needed, future developments may allow for seamless adaptation of BLTs in current AI workflows without retraining from scratch.

The Byte Latent Transformer (BLT) represents a significant shift in how models can process raw data at the byte level. By moving away from fixed tokens and using dynamic patches based on entropy measures, BLT offers a more flexible and efficient approach to handling diverse data and computational needs. This method allows for a more granular understanding of the data, better computational efficiency, and improved flexibility in handling various input formats.

BLTs have significant potential but also require further optimization, larger-scale testing, and software improvements to reach their full efficiency. Future work on scaling laws, model patching, and integration with existing deep learning frameworks could help overcome these challenges.

While BLTs are still evolving, early results suggest they can match or even outperform traditional transformer models at scale. As AI continues to push the boundaries of efficiency and adaptability, BLTs could play a crucial role in shaping the future of natural language processing.