Published on September 29, 2026, the summary describes a study on transferring knowledge from pretrained Transformers to Mamba models and notes memory and throughput advantages for SSMs.
Published on September 29, 2026, the article studies using pretrained Transformers to train state space models (SSMs), such as Mamba. Its summary points to lower memory use and higher generation throughput for SSMs compared with attention-based models; it provides no figures or experimental details.
The work addresses how to transfer knowledge already gained by training Transformers to SSMs. To verify the scope and evidence, consult the original article and compare its summary, method, and results; the supplied material contains no bibliographic details or address. If you use AI to study the topic, follow your organization’s data policy and avoid entering confidential information.