Popper

l program synthesis systems are trained on examples that consist of pairs of (programming-task, program-solving-the-task). Another way to see the difference between Inductive Programming systems and conventional machine learning systems used for program synthesis is that Inductive Programming systems learn by solving problems rather than from observing solutions. The advantage is that, in this way, we can learn programs that we don't know how to write (because we don't have to generate examples of such programs) whereas with conventional machine learning we can only generate programs like the ones the system's been trained on before. Another advantage is that it's much easier to generate examples. For instance, if I want to learn a program that reverses a list, I give some examples of lists and their reverse, e.g. reverse([a,b,c],[c,b,a]) and reverse([1,2,3],[3,2,1]) whereas e.g. an LLM must be trained on explicit examples of list-reversing programs; like, their source code. IFP and ILP systems are also very sample efficient, so they only need a handful of examples, often just one, whereas neural net-based systems may need millions (no exaggeration- can give a ref if needed). The disadvantage is that learning a program usually (but not always - see Louise, above) implies searching a very large combinatorial space and that can get very expensive, very, very fast. But, there are ways around that.