arXiv:2605.21292v1 Announce Type: cross Abstract: Gradient-flow analyses show that simplified linear transformers can learn the in-context linear-regression algorithm, but they do not explain the finite-step behavior of gradient descent at large learning rates. Motivated by empirical work on high-learning-rate transformer instabilities and by the cubic-map phase diagram for quadratic regression, we study an exactly reducible one-prompt linear-transformer training problem. After normalization, the dynamics reduce to a two-factor product map with an effective step-size parameter \(\mu\). On the
This paper explores advanced theoretical aspects of transformer training dynamics, addressing limitations in current understanding of large learning rates, which aligns with ongoing research in optimizing AI models.
A strategic reader interested in the fundamental science behind AI model training might find this important for future algorithmic advancements, but it has no immediate practical implications.
No immediate change, but it contributes to the theoretical foundation that could, over a long horizon, inform better AI model design and training methodologies.
Refined theoretical understanding of transformer training at high learning rates.
Improved efficiency or stability in future large language model development.
Potentially faster training times or more robust AI models if theoretical insights are applied practically.
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