The paper examines catastrophic loss spikes during low-precision training with Flash Attention. It associates these failures with similar internal patterns in attention data and biased rounding errors that accumulate.
When investigating instability, first record when the loss spikes and under what training conditions. Compare runs and precision settings without changing many conditions at once; this can reveal correlations, though it does not prove the cause.
Check whether attention data has similar patterns in segments associated with failures. Also examine whether rounding errors may be accumulating in a biased way. These are clues described by the paper, not a guaranteed fix for every unstable training run.
If you use AI to study the paper or analyze logs, send only the material needed and remove internal data, identifiers, and credentials. Do not include weights, training data, or confidential logs without authorization; check your organization’s policy before sharing content.