My second mistake was thinking that danger was related to the quantity of RL finetuning. I muddled up agency/goal-directedness with danger, and was also wrong that RL is more likely to produce agency/goal-directedness, conditioned on high capability. It's a natural mistake, since stereotypical RL training is designed to incentivize goal-directedness. But if we condition on high capability, it wipes out that connection, because we already know the algorithm has to contain some goal-directedness.
Distinguish two notions of "goal-directedness":
The system has a fixed goal that it capably works towards across all contexts.
The system is able to capably work towards goals, but which it does, if any, may depend on the context.
My sense is that a high level of capability implies (2) but not (1). And that (1) is way more obviously dangerous. Do you disagree?