Beyond Language Models: Byte Models Are Digital World Simulators (2024) (arxiv.org)

A groundbreaking development in machine learning has emerged with the introduction of bGPT, a model designed for next byte prediction that simulates the digital world at a fundamental level. While traditional language models focus on textual sequences, bGPT leverages bytes — the basic units of digital information — to demonstrate high-performing capabilities across various modalities, including text, audio, and images. With an impressive accuracy rate of over 99.99% in simulating CPU behavior and an error rate of just 0.0011 bits per byte when converting symbolic music data into MIDI format, bGPT opens new avenues for predicting and diagnosing algorithmic and hardware functions. This advancement is significant for the AI/ML community as it enables researchers and developers to explore beyond the constraints of conventional language processing, providing a deeper understanding of how digital information interacts and functions. By focusing on next byte prediction, bGPT not only enhances simulation and predictive modeling but also offers valuable insights into optimizing algorithms and hardware performance. As AI continues to evolve, innovations like bGPT highlight the potential for machine learning models to engage with the digital realm more intricately, driving further exploration and applications in the field.