Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors
arXiv:2606.25971v1 Announce Type: new Abstract: Modern neural network training relies on optimizers such as Adam and Muon which act on each weight matrix as a single object. Yet every weight matrix carries two distinct quantities -- a \emph{magnitude} and a \emph{direction} -- and all optimizers stepping in the matrix as a whole couple their dynamics: the directional change from an update depends on the current magnitude, while the magnitude drifts as a byproduct of learning the direction, so neither is governed directly by the learning rate. Typical training therefore leans on surrounding rec
This research is emerging as AI model complexity and training costs continue to rise, pushing the need for more efficient optimization techniques.
Improved neural network training efficiency can lead to faster development, lower computational costs, and potentially enable larger, more capable AI models.
Optimizers might evolve to explicitly decouple magnitude and direction, leading to more stable and efficient training of deep learning models.
- · AI researchers
- · Cloud AI providers
- · Deep learning practitioners
- · Hardware manufacturers
- · Inefficient AI training methods
- · High-cost compute centers
More sophisticated and efficient neural network optimizers will be developed and adopted.
This could accelerate the development of advanced AI models across various applications, reducing the time and resources required for breakthroughs.
Lowering the barrier to entry for training advanced AI could democratize AI development, fostering innovation beyond current few large players.
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Read at arXiv cs.LG