I’m not sure if I follow this or not. But it rhymes with a general picture I have of sequential planning often being hierarchical: you get good at the small pieces, those become available abstractions to plan with, and now you’re planning in ‘option’ space (as opposed to raw ‘action’ space) which is fully recursive. You need some amount of evidence for how options work (which might be policy-/actor-dependent) and how to strategise in that ‘higher’ space. You build your library of option concepts through learning and abstraction. Starting from scratch, you need to build up from the atomic action level. If you’re seeded with lots of approximate examples across the hierarchy (as are LMs—and probably humans to some extent), the dynamics are a bit different (but perhaps not very different unless your starting base is very ‘hierarchy-level-asymmetrical’).
I’m not sure if I follow this or not. But it rhymes with a general picture I have of sequential planning often being hierarchical: you get good at the small pieces, those become available abstractions to plan with, and now you’re planning in ‘option’ space (as opposed to raw ‘action’ space) which is fully recursive. You need some amount of evidence for how options work (which might be policy-/actor-dependent) and how to strategise in that ‘higher’ space. You build your library of option concepts through learning and abstraction. Starting from scratch, you need to build up from the atomic action level. If you’re seeded with lots of approximate examples across the hierarchy (as are LMs—and probably humans to some extent), the dynamics are a bit different (but perhaps not very different unless your starting base is very ‘hierarchy-level-asymmetrical’).