Nous Research has published a study indicating that tokenizers, traditionally used in large language models, could soon be obsolete. The research, conducted on a 1.7 billion parameter model, demonstrated that the benefits of tokenization can be effectively simulated at the byte level. By increasing data throughput and integrating morphological boundaries into byte-based models, the performance gap was significantly reduced. The study found that simulated compression enhanced gradient processing per step, leading to a notable reduction in validation loss. The research also explored encoding subword boundaries as binary sequences, establishing a long-term inductive bias without leaking future information. While the effects at larger parameter scales need further validation, the study at the 1.7 billion scale showed limited benefits from vocabulary parameter scaling and other mechanisms like subword prediction. This suggests a shift towards tokenizer-free models, focusing on throughput and morphological priors.