Efficient Transformers: A Survey

Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision, and reinforcement learning. Recently, a dizzying number of “X-former” models have been proposed—Reformer, Linformer, Performer, Longformer, to name a few—which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency .

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In the field of natural language processing for example, Transformers have become an indispensable staple in the modern deep learning stack.
• 2 Recently, a dizzying number of “X-former” models have been proposed—Reformer, Linformer, Performer, Longformer, to name a few—which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency .
• 3 With the aim of helping the avid researcher navigate this flurry, this article characterizes a large and thoughtful selection of recent efficiency-flavored “X-former” models, providing an organized and comprehensive overview of existing work and models across multiple domains.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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