Determinism in Training

An AI model is not created by programming, but by learning from examples. This learning process is called training. During it, a computer crunches numbers for weeks until the model masters the task. Determinism in training means: if you start this process a second time with the same examples and the same settings, you get exactly the same model. That sounds self-evident, but it isn’t. In practice, two runs almost always differ slightly, even though no one changed anything.

Why researchers depend on identical runs

Science relies on the ability to verify results. If a team claims that a new method makes models three percent better, another team must be able to check this by recalculating it. If the training is not repeatable, no one knows whether the improvement was due to the method or simply luck. This is exactly a well-known problem in AI research: many published improvements vanish when others try to reproduce them.

Repeatability is also practically useful during development itself. Suppose a model suddenly behaves worse after a change. Only if everything else has stayed the same can the change be identified as the cause. Otherwise, one searches for the error in noise made up of random fluctuations. Developers refer to this as debugging, i.e., the systematic search for errors.

There is also a legal aspect. Anyone deploying AI in medicine, banking, or government agencies often must be able to prove how the model came into being. A regulatory authority can require that a training run be documented in a traceable manner. A model whose creation cannot be reproduced is hard to defend in such areas.

Where the small deviations come from

Random numbers are deliberately used at many points in training. The model’s initial values are random, as is the order of the training examples. However, computers only generate such random numbers in an apparently random way: they compute them from a starting number, the so-called seed. If you set the same seed, you get the same sequence of numbers. This part of the problem can therefore be easily brought under control.

The computing hardware is more difficult. Training is done on graphics cards that execute thousands of computing steps simultaneously. Which one finishes first depends on the workload and fluctuates minimally. And computers calculate with decimal numbers only with imprecise rounding. Adding the same numbers in a different order produces a tiny different result. Over billions of computing steps, this difference grows.

You can force graphics cards to keep the order fixed. But that costs speed, sometimes ten percent or more. That’s why experts distinguish two levels of ambition. Bit-for-bit determinism means: the result is identical down to the last digit. Statistical determinism only means: the model’s quality is practically the same, but the numbers inside are not. The second goal is cheaper and sufficient for many purposes.

Determinism in model cards and audit reports

Research papers on AI almost always include a paragraph on this topic. It states the seed used, the software versions, and the type of graphics cards. Authors often report results as an average across several runs with different seeds, along with the range of variation. This is more honest than a single number from the best run.

In companies, the term comes up in connection with the traceability of models, known in English as reproducibility. Large providers log every training run: dataset, settings, timestamp, hardware. The European AI Act requires similar documentation for high-risk applications. Complete determinism is rarely the goal here—good logs are.

A common misconception: determinism in training has nothing to do with a chatbot giving different answers to the same question. That concerns the use of the finished model, not its creation. There, the variation is usually even intentional, so that answers don’t sound monotonous.