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February 19, 2025
Researchers have introduced MUDDFormer, short for MUltiway Dynamic Dense Former, a new method that significantly improves the performance of Transformer models. The key lies in optimizing the information flow between the different layers of the network. Traditional Transformer architectures rely on residual connections, which can be limited in their effectiveness. MUDDFormer addresses these limitations by introducing dynamic and multiway dense connections.
Residual connections in Transformer models allow information to be passed directly from one layer to the next, avoiding the so-called "vanishing gradient" problem and enabling the training of deeper networks. However, these connections can also lead to bottlenecks in information flow, especially when the information to be processed becomes more complex. The static nature of residual connections prevents flexible adaptation to the specific requirements of the data.
MUDD connections offer a solution to this challenge. In contrast to static residual connections, MUDD generates connection weights dynamically, based on the hidden states at each position of the input sequence. This dynamic approach allows for more precise control of the information flow and better adaptation to the specific characteristics of the data. Furthermore, MUDD considers the different input streams of a Transformer block – Query, Key, and Value – separately, which enables further performance improvement.
A key advantage of MUDDFormer is its seamless integration into existing Transformer architectures. The MUDD connections can be implemented without major modifications, which facilitates application in various contexts. Experiments have shown that MUDDFormer achieves significant performance gains in various model architectures and sizes. The method scales well and allows comparable or even better results than conventional Transformers to be achieved with less computational effort.
MUDDFormer has shown particularly impressive results in the field of language model training. Tests demonstrate that MUDDFormer achieves the performance of Transformers trained with 1.8 to 2.4 times the computational effort. Specifically, MUDDPythia-2.8B was able to keep up with Pythia-6.9B in pretraining and downstream tasks and even compete with Pythia-12B in five-shot scenarios, despite requiring only 0.23% additional parameters and 0.4% additional computational effort.
MUDDFormer represents a promising advancement in Transformer architecture. By introducing dynamic and multiway dense connections, the information flow within the network is optimized and performance is significantly improved. The easy integration and scalability make MUDDFormer an attractive option for various applications in the field of machine learning. The release of the code in JAX and PyTorch, along with pretrained models, allows researchers and developers to test and further develop the technology themselves.
Bibliographie: - Hugging Face - Papers - arxiv:2502.12170 - MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections