arXiv:2606.16768v1 Announce Type: new Abstract: Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) theory provides a powerful tool to understand and control the stability of optimization methods via the (preconditioned) curvature, these curvature-controlling methods are not popular in large-scale Transformer training due to the complexity of curvature estimation. To this end, we first introduce a fast online estimator of the largest (preconditioned) Hessian eigenvalue
The increasing scale and complexity of Transformer models necessitate more robust and efficient training methods to overcome prevalent stability issues and computational waste.
Improved stability and efficiency in training large-scale Transformers can accelerate AI development, reduce compute costs, and democratize access to advanced AI capabilities.
This research provides a practical method for taming curvature in Transformer training, potentially making multi-billion parameter models easier and cheaper to train successfully.
- · AI model developers
- · Cloud computing providers
- · Deep learning researchers
- · Generative AI startups
- · Inefficient AI training methods
- · Compute resources wasted on unstable runs
More stable and resource-efficient training of large language models and other Transformer architectures.
Faster iteration cycles and lower costs for developing and deploying cutting-edge AI models.
Accelerated AI advancement could lead to a broader proliferation of powerful AI agents and applications across industries.
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Read at arXiv cs.LG