AoT/JIT work would literally be the training tool to train the model, with additional optimizations along the way. The issue is that manual removal of all the r

AoT/JIT work would literally be the training tool to train the model, with additional optimizations along the way. The issue is that manual removal of all the runtime stuff from the generated code is just cumbersome right now, but with ML, its just a matter of having enough examples. The advantage is that you get native optimized code like someone wrote it in C directly, and ability to automatically generate code to be offloaded to GPU as people start doing expanded training with higher level pattern recognition. The incorectness part I adressed already, stochastic output doesnt matter when your domain is finite.