The Transformer's self-attention architecture scales unusually well with large data, large models, and parallel training — which made it practical to train models like GPT, BERT, T5, and LLaMA at massive scale. The arc runs from RNNs to LSTM/GRU to encoder-decoder architectures to attention to Transformers to pretrained language models to modern LLMs, with each stage removing a bottleneck the previous one had.

The Transformer solved the core limitations RNNs had: it processes tokens in parallel instead of sequentially, captures long-range dependencies through self-attention instead of a fading hidden state, and trains efficiently on massive datasets because parallelism is a much better fit for GPU/TPU hardware. Several things compounded to turn that architecture into modern LLMs: parallel training made large-scale training practical in the first place; self-attention improved context understanding across long sequences; scalability meant larger models and more data reliably improved performance rather than plateauing; pretraining let models learn general language patterns from huge, unlabeled text corpora; and a decoder-only variant made large-scale next-token prediction the natural training objective. From there, modern LLMs are refined further with fine-tuning, instruction tuning, and reinforcement learning from human feedback (or newer preference-optimization methods), plus tool use and RAG integration in production systems — but the foundation underneath all of it is still self-attention plus scale.