RetNet combines parallel training with recurrent inference
The Retentive Network architecture proposes a retention mechanism and parallel, recurrent, and chunkwise recurrent computation paradigms, connecting recurrence with attention.
The article on Retentive Network, or RetNet, presents an architecture for language models based on a retention mechanism. The proposal connects recurrence and attention.
The work describes three computation paradigms: parallel, recurrent, and chunkwise recurrent. The text gives no quantitative results and does not claim that one mode is best in every setting.
To assess the proposal, compare these computation modes with your task requirements and the metrics that matter in your environment. Distinguish what the article describes from what you need to measure in your own tests.
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