Hawk and Griffin Models: Superior NLP Performance with Minimal Training Data | HackerNoon
Briefly

The study demonstrates that recurrent models can scale as efficiently as transformers, which potentially shifts paradigms in computational efficiency for machine learning tasks.
By using a model parallelism approach in training, researchers have achieved impressive training speeds, optimizing performance even on longer sequences, which traditionally posed challenges.
The findings regarding next token prediction highlight how longer contexts significantly enhance model performance, underlining the importance of context length in generating coherent outputs.
Techniques developed in this research, such as the Efficient Linear Recurrences on Device, provide promising pathways for enhancing the utility of recurrent models in practical applications.
Read at Hackernoon
[
|
]