RNNs and LSTMs process tokens sequentially, one at a time, which makes training slow and makes distant relationships hard to capture since information has to travel through every intermediate step. Transformers use self-attention to compare tokens directly across the full sequence and train in parallel rather than step by step.

Older RNNs and LSTMs process text sequentially: training is inherently step-by-step, and long-range dependencies degrade because information has to be carried forward through every intermediate token. Transformers compare tokens directly through self-attention regardless of distance, so long-range context doesn't degrade the same way, and all tokens can be processed simultaneously during training rather than one at a time. That parallelism is also why transformers scale so predictably with more data and compute, while RNNs did not scale as reliably — and the matrix multiplications attention relies on map naturally onto GPU/TPU hardware. On translation, summarization, generation, and reasoning tasks, transformers consistently outperform RNN-based architectures at scale, which is why virtually all modern LLMs are transformer-based.