Can rounded loss values prove two runs are identical?
No. Printed losses are rounded summaries and can match even when underlying tensors differ. To locate divergence, compare ordered fingerprints of intermediate tensors and checkpoints at consistent points. Matching visible metrics is useful but does not establish identical internal numerical states.
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Why Two Identical AI Training Runs Can Still DisagreeBitwise reproducibility requires matching the full training setup and operation order, not merely reusing the same random seed.
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