Same State, Different Stream: Why Reproducible AI Needs More Than a Seed
TL;DR for operators A fixed seed is not a complete reproducibility guarantee. More surprisingly, neither is restoring the complete state of the random-number generator. In Reproducible AI Requires Reproducible Randomness, Anthony Bertrand, Tom Schmitt, Engelbert Mephu Nguifo, and David Hill compare library implementations of Mersenne Twister and Philox against canonical reference generators.1 Their deterministic tests show that the same nominal generator, initialized from the corresponding full state, can still emit a different sequence because libraries change what happens between stored state and returned output. ...