arXiv:2606.27715v1 Announce Type: new Abstract: We show that for tall matrix parameters, like projection matrices in the MLP layers, the Muon update can have row norms that are arbitrarily non-uniform. This can lead to a self-reinforcing feedback loop whereby neurons receive persistently small updates and eventually do not contribute meaningfully to network outputs. This problem is effectively mitigated by an additional row normalization step, but current methods do this in a way that moves the Muon update geometry away from the polar factor of the momentum matrix, which we find is undesirable
This paper addresses a known challenge in training deeper and more performant neural networks by proposing an improved optimization technique that specifically tackles non-uniform updates in MLP layers.
Improved spectral optimizers like Aurora can lead to more stable and efficient training of large language models and other deep learning architectures, potentially accelerating AI development and performance.
The proposed 'leverage-aware' spectral optimizer offers a more effective way to normalize updates, preventing neuron 'death' and allowing networks to utilize their full capacity during training.
- · AI researchers
- · Deep learning practitioners
- · Companies developing large AI models
- · Developers relying on suboptimal optimization techniques
Aurora could become a standard optimization technique, leading to quicker training times and improved model performance.
More efficient training could reduce the computational resources needed for developing cutting-edge AI, democratizing access to powerful models to some extent.
The ability to train even larger and more complex models efficiently could further accelerate progress towards advanced AI agents and capabilities.
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Read at arXiv cs.LG