arXiv:2605.24425v1 Announce Type: cross Abstract: The residual update of a pre-norm Transformer layer admits an interpretation as one step of a first-order optimizer acting on a surrogate token energy, wherein the attention and MLP sublayers function as gradient oracles. Based on this observation, we build a family of optimizer-inspired Transformers (triple-momentum, Adam/AdamW, Muon, SOAP) and compare them under matched compute. In our main pretraining experiment, the triple-momentum TMMFormer achieves the lowest validation loss, outperforming the vanilla Transformer and prior architectural v
This research is emerging as the field continues to seek more efficient and performant Transformer architectures, driven by ongoing demands for improved AI models.
The development of optimizer-inspired Transformers potentially offers significant performance gains and computational efficiencies, which are critical for advancing large-scale AI capabilities and reducing compute requirements.
New Transformer architectures based on momentum optimizers can lead to more effective training and potentially smaller, faster, or more capable AI models compared to traditional designs.
- · AI model developers
- · Cloud compute providers via efficiency
- · Hardware manufacturers via new demand patterns
- · Developers reliant on older Transformer architectures
- · Less efficient AI training methods
Improved performance and efficiency in AI models become more accessible and widespread.
Accelerated development of more complex and capable AI systems across various applications.
Increased competition among foundational model providers due to more accessible high-performance architectures.
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Read at arXiv cs.CL