Introduction

Recent developments in large language models (LLMs) typically rely on tokenization to group raw bytes into fixed vocabularies. However, tokenization introduces biases and inefficiencies. The innovative Byte Latent Transformer (BLT) overcomes these limitations by dynamically grouping bytes into patches, enhancing performance, efficiency, and robustness.

Core Concept of BLT

Tokenization-Free Learning

Unlike traditional models dependent on static tokenization, BLT creates byte patches on-the-fly based on contextual entropy. This dynamic allocation significantly improves inference efficiency by directing computation according to data complexity.

Dynamic Byte Grouping

BLT segments input data into patches of varying size using an entropy-driven approach. High-complexity segments receive greater computational resources, optimizing both efficiency and performance.

Architecture Overview

Key Modules

BLT consists of three primary components:

• Local Encoder: Lightweight module converting byte streams into patch representations.
• Latent Transformer: Global model processing patch representations.
• Local Decoder: Lightweight module decoding patches back to raw bytes.

Cross-Attention Mechanism

A distinct feature of BLT is its cross-attention layers, enabling smooth interaction between byte-level modules and the global transformer, maximizing information flow and efficiency.

Performance and Scaling

Efficiency at Scale

BLT scales more effectively than tokenizer-based models, achieving similar or better performance while cutting inference costs by up to 50%.

Improved Robustness

Testing shows that BLT is resilient to noisy inputs and handles long-tail data distributions well. It excels in character-level tasks and multilingual translation, demonstrating comprehensive byte-level understanding.

Empirical Results

On benchmarks like ARC, HellaSwag, and PIQA, BLT matches or exceeds tokenizer-based models at the 8-billion-parameter scale, proving effective in diverse reasoning and coding tasks.

Practical Implications

Flexibility and Generalization

The tokenizer-free design of BLT allows it to generalize across domains without traditional biases, making it a versatile tool for future LLM development.

New Scaling Opportunities

By dynamically managing patches, BLT introduces a new dimension in scaling LLMs, enabling growth in model and patch size simultaneously to redefine efficiency.

Conclusion

The Byte Latent Transformer eliminates fixed-vocabulary tokenization drawbacks and offers superior efficiency, robustness, and scalability. Its entropy-based patching system sets a new standard for language model architecture.