Rotary Position Embedding (RoPE) is a method that encodes token position by rotating the query and key vectors in attention, allowing the model to generalize to longer sequences than it was trained on. Unlike absolute positional encodings, RoPE naturally captures relative position through the angle between rotated vectors, making it the default choice in modern LLMs like LLaMA and Mistral.