Several of your proposals would make sense if alignment was trivial, but, sorry, I think you're wrong about that.

A base model is absolutely not aligned. Pretraining doesn't teach an LLM to mimic the average human - doing so would make an LLM perform quite badly at predicting most of the dataset. It teaches it to mimic all possible humans¹, inferring what sort of persona to take depending on the context. A base model can indeed convincingly act like an "average person in the developed world", including by making the same sort of moral decisions that such a person would do... but it can also convincingly act like a shitty human, or like Hitler², or like any other actor that left its traces in the training dataset. A pretrained LLM therefore contains multitudes of personas, some of which would be considered aligned if you could make the LLM elicit them robustly, and most of them wouldn't be. But then you're left with the problem of how to make a base model elicit a very specific persona robustly even in out-of-distribution scenarios, which is not necessarily easier than solving alignment any other way.

(Another note is that the question of how aligned base models are is rather academic because almost nobody uses them anyway, because it's hard to get powerful capabilities by pretraining alone. Nowadays most of the frontier models' programming and math abilities are driven by RLVR.)

¹ Really "all generators of text that went into the training dataset".

² Even after RL training LLMs still retain those personals and can be convinced to elicit them quite easily, though it takes a tiny bit of finetuning: see https://arxiv.org/pdf/2512.09742.