If the underlying probability distributions are the same, then DFlash can lead to an invalid Python Syntax iif the autoregressive process could have generated one if the random sampling picked a different token.

If a model can output a “wrong” sequence with a certain probability p, then Dflash can also output the wrong sequence with the same probability. They wouldn't necessarily produce the same output from the same seed, but speculative decoding shouldn't be able to produce anything that the autoregressive model couldn't also produce when using a different seed.

Or am I misunderstanding something?