The recent advancements in language modeling and tokenization have significantly shifted the focus towards efficiency and robustness. Researchers are increasingly exploring novel methods to enhance the performance of large language models (LLMs) while reducing computational resources. One prominent trend is the development of dynamic token merging techniques, which aim to shorten input sequence lengths without compromising model accuracy. These methods leverage contextual information to merge critical tokens, thereby improving both training and inference efficiency. Additionally, there is a growing interest in variable-length tokenization inspired by data compression algorithms, which promises to train LLMs more efficiently with less data. These innovations not only address the practical limitations of existing models but also open new avenues for cross-linguistic and cross-domain applications. Furthermore, the exploration of morphological typology in tokenization reveals correlations between language structures and model performance, suggesting that understanding linguistic features can lead to more effective tokenization strategies. Notably, the field is also witnessing a critical examination of vulnerabilities in byte-level tokenizers, highlighting the need for robust and trustworthy models. Overall, the current research landscape is characterized by a push towards more efficient, adaptable, and secure language models.