My impression is that, while "set up an appropriate RL environment, given that you have deep domain expertise" is a narrow skill, you can't actually escape the part where you need to understand the particulars of the domain (unless your domain is one of the few where a simple non-lossy success metric exists). Whether you can build a curriculum to teach a domain to an AI if neither you nor the AI have significant experience in that domain is a major crux for me. [...] I am extremely not convinced that this is a single learnable cross-domain skill rather than something where the skill of figuring out which lessons are the correct ones varies on a domain-by-domain basis [...] There's a form of this that seems generalizable (e.g. noticing training stability issues, knowing which loss schedule / hparams to use, avoiding known footguns in the common RL frameworks) and a form that doesn't (e.g. noticing when the student model has learned a skill that scores well due to quirks of your RL environment which won't transfer to the real world). [...] Yes. But the bit where recursive self improvement is a major differentiating factor because an AI can learn a non-transferable "get better at the general skill of teaching yourself things" seems to me like it's on shakier ground. I think the schleppy path of "learn skills by intentionally training on those specific skills" will be the main way AIs get better in the next few years. Which has many implications, particularly something like "frontier models can get good at any skill you care to name, but they can't get good at every skill".