The evolution of large language models (LLMs) underscores the complexity and importance of pretraining in shaping their capabilities and behaviors. Initially delineated by ULMFiT and formalized by InstructGPT, pretraining has become a pivotal stage in NLP, transitioning from basic next-token prediction to sophisticated, instruction-following models. Despite its foundational role, the pretraining process is often inconsistently defined, with its boundaries blurring as models evolve to include multi-phase and continual pretraining, instruction-augmented data, and innovative methods like reinforcement pretraining. These advancements aim to enhance model performance, alignment, and adaptability to new knowledge and domains, emphasizing the dynamic nature of LLM training. The shift from static pretraining datasets to more strategic data curation and curriculum learning further complicates the landscape, highlighting the ongoing challenges of maintaining ethical standards and data quality. As models grow in sophistication, the balance between model size and data volume, as demonstrated by Chinchilla's efficiency over larger models like Gopher, becomes a critical consideration. Ultimately, while the pretraining paradigm continues to evolve, the principles laid down by early models remain essential for navigating this rapidly advancing field.