A new language model architecture called Bolmo has been introduced, designed to enable efficient training at the byte level without compromising output quality. Traditional models typically use subword tokenization, which can be inefficient and limit the model’s ability to handle diverse data formats like code or non-Latin scripts. Bolmo addresses this by employing a novel, simplified transformer architecture that operates directly on raw bytes, eliminating the need for a tokenizer. This approach reduces computational overhead and memory usage during training while maintaining competitive performance on standard benchmarks compared to larger, token-based models. The architecture’s efficiency could make advanced language model training more accessible and adaptable to a wider range of data types. Read the full article at https://venturebeat.com/ai/bolmos-architecture-unlocks-efficient-byte-level-lm-training-without.