RetNet combines parallel training and recurrent inference
Published on September 24, 2026, the article presents the Retentive Network, a language-model architecture proposing parallel training alongside recurrent and chunkwise recurrent inference paradigms.
The article on the Retentive Network presents a language-model architecture based on a retention mechanism. It describes parallel, recurrent, and chunkwise recurrent computation paradigms, and derives a connection between recurrence and attention.
The proposal concerns sequence modeling and inference approaches, topics relevant to people evaluating Transformer architectures. To check the details and verify the findings, consult the original article, compare its claims with the text, and examine the methods and evidence it presents.