New method predicts transformer training divergence before the run starts
burkov · x · 2026-09-06
Andriy Burkov highlights a new paper tackling a costly problem: large transformer training runs often fail from sudden divergence, and instability is usually discovered only after compute has been burned.
The work proposes a practical way to estimate, before training begins, the probability that a given configuration will diverge, and turns that estimate into an intervention that prevents failure while allowing more aggressive optimization. An AI-tutor reading link is included.
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