This paper works because it
explicitly is a problem domain that was intentionally constrained to ensure safety in the Amateur high-power rocket hobby. Specifically with constraints and standards that were developed for teenagers of various skill to do with paper and pen well before they had access to containers. While modern applications have added more functions, those core constrains remain.
It works explicitly because it doesn't hit the often counter-intuitive limitations with generalization in pure math.
Remember that Boolean circuit satisfiability is NP-complete, and is beyond UHAT's + poly length CoT expressibility, which is capped at PTIME.
Even int logic with boolean circuits is in PSPACE.
When you start to deal with values, you are going to have to add in heuristics and/or find reductions that will cost your generalizability.
Even if you model analog circuits as finite labelled directed graphs with labelled vertices, similar to what Shannon used; removing some of the real world electrical impacts and focus on them as computational units, the complexity can get crazy fast.
Those circuits, with specific constraints (IIRC local feedback, etc..) can be simulated by a Turing machine, but require ELEMENTARY space or time, and despite it's name ELEMENTARY is iterated exponential: 2^2^2^2^2^...^n with k n's.
Also note that P/poly, viewed as problems that can be solved by small circuits is not a practical class and in fact contains all of the unary languages that we know are unsolvable by real computers in the general case.
That apparent paradox that P/poly, which has small bool circuits, also contains all of those undecidable unary languages is a good starter into that rat hole.
While we will have tools and models that are better at math logic, the constrains are actually limits on computation in the general case. Generalization often has these types of costs, and the RL benefits in this case relate to demonstrating that IMHO.