Because the execution trace is part of the forward pass, the whole process remains differentiable: we can even propagate gradients through the computation itself. That makes this fundamentally different from an external tool. It becomes a trainable computational substrate that can be integrated directly into a larger model.
[...] *the compilation machinery we built for generating those weights** can go further. In principle, arbitrary programs can be compiled directly into the transformer weights, bypassing the need to represent them as token sequences at all. [...] [my emphasis]
Weights become a deployment target: instead of learning software-like behavior, models contain compiled program logic.
If logic can be compiled into weights, then gradient descent is no longer the only way to modify a model. Weight compilation provides another route for inserting structure, algorithms, and guarantees directly into a network.