The result is a fuzzy reproduction of the training input, specifically of the compilers contained within. The reproduction in a different, yet still similar enough programming language does not refute that. The implementation was strongly guided by a compiler and a suite of tests as an explicit filter on those outputs and limiting the acceptable solution space, which excluded unwanted interpolations of the training set that also result from the lossy input compression.

The fact that the implementation language for the compiler is rust doesn't factor into this. ML based natural language translation has proven that model training produces an abstract space of concepts internally that maps from and to different languages on the input and output side. All this points to is that there are different implicitly formed decoders for the same compressed data embedded in the LLM and the keyword rust in the input activates one specific to that programming language.

"clean room" usually means "without looking at the source code" of other similar projects. But presumably the AIs training data would have included GCC, Clang, and probably a dozen other C compilers.