How is this different from the sinusoidal embeddings used in "Attention is All You Need"?
A response many of us at EleutherAI had when first coming across this was "how does this differ from sinusoidal embeddings," so we feel it is worth discussing this comparison. There are two ways that rotary embeddings are different from sinusoidal embeddings:

Sinusoidal embeddings apply to each coordinate individually, while rotary embeddings mix pairs of coordinates
Sinusoidal embeddings add a or term, while rotary embeddings use a multiplicative factor.
Okay, what About in Practice?
After reading NAME_1's original blog posts [12, 13], we were curious how well such a first-principles approach to positional encoding would stack up against existing methods. Despite a tremendous number of papers that have come out claiming to improve the transformer architecture, very few approaches generalize well across codebases and tasks. However, we have found that rotary positional embeddings perform as well or better than other positional techniques in every architecture we have tried.

Write a brief summary, and explain in plain language Write exactly 3 paragraphs each separated with two new lines answering this question. The first paragraph must start with "offer".
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