The Byte Latent Transformer (BLT) is a novel byte-level large language model (LLM) that processes raw byte data by dynamically grouping bytes into entropy-based patches, eliminating the need for tokenization.
- Dynamic Patching: BLT segments data into variable-length patches based on entropy, allocating more computation where complexity is higher—unlike token-based models that treat all tokens equally.
- Efficiency & Robustness: BLT matches tokenized LLM performance while improving inference efficiency (using up to 50% fewer FLOPs) and enhancing robustness to noisy inputs and character-level tasks.
- Scalability: Scaling studies up to 8B parameters and 4T training bytes show that BLT achieves better scaling trends at a fixed inference cost than token-based models.
- Architecture:
- Entropy-Based Patching: A small byte-level model estimates entropy to determine patch boundaries, allocating more compute to complex sequences (e.g., word beginnings).
- Performance Gains: BLT achieves parity with Llama 3 in FLOP-controlled training and outperforms it in character-level tasks and low-resource translation.
- Patch Size Scaling: Larger patches (e.g., 8 bytes) improve scaling efficiency by reducing latent transformer compute needs, enabling larger model sizes within a fixed inference budget.
- "Byte-ifying" Tokenizers: Pre-trained token-based models (e.g., Llama 3.1) can initialize BLT’s transformer, leading to faster convergence and improved performance on specific tasks.
BLT introduces a fundamentally new approach to LLMs, leveraging raw bytes instead of tokens for more efficient, scalable, and robust language modeling.
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