RetNet
Definition
RetNet combines parallel training with state-based stepwise processing.
A sequence model architecture with retention mechanism that combines parallel representation for training, recurrent representation for stepwise inference, and a chunkwise recurrent variant.
Simply explained
The model can process many parts jointly during learning and pass on a compact intermediate state when responding.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A long sequence model processes tokens recurrently with limited state during inference.
Limits and pitfalls
- Equating retention with classical RNN architecture, presenting linear inference costs as a general speed guarantee, or transferring research results without verification.
Origin & history
Sun and co-authors published RetNet in 2023.
Use in mid-sized companies
- Possible application: A long sequence model processes tokens recurrently with limited state during inference.
Frequently asked questions
What does RetNet mean?
A sequence model architecture with retention mechanism that combines parallel representation for training, recurrent representation for stepwise inference, and a chunkwise recurrent variant. Which mistakes should be avoided? Equating retention with classical RNN architecture, presenting linear inference costs as a general speed guarantee, or transferring research results without verification.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: