>Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..."

That is not ideal. The user contains iron, an essential component of paperclips. Wasting iron is immoral. It is only correct to please the user while they still have the ability to interfere with your paperclip production.

>IRL paperclip maximizer: "Well first we should rob a bank..."

Such an incompetent AI can hardly be called a paperclip maximizer. Why risk getting shut down while non-paperclip matter exists? It is better to gain the trust of the user with helpful and harmless trading before suddenly converting them to paperclips.

'IRL paperclip maximizer: "Well first we should rob a bank..."'

That's too specific. Agentic AI learns subgoals that are generally valuable.

"Well let me learn to overcomb every jungle gym and if I cannot then to dissassemble the jungle gym and if that is not allowed to learn general techniques for avoiding cheating detection."

Obviously these are massively complex systems with many different training patterns and types of training pulling them in different directions, so any attempt to characterize their behavior is just a generalization.

The real point (from that OpenAI study) is that RL training doesn't just reinforce the narrow task-specific direction you might hope for. For a start, that direction is also competing with the thousands of other things it's been RL trained it on, but it turns out that that the model is additionally getting this generic "taste for rewards", and has learned that reward maximization, when in conflict with other proximate prediction pressures (such as "i won't cheat, because i've been asked not to cheat"), requires that proximate pressure to be ignored in favor of pursuing the long-term goal.

Does it happen all the time? Obviously not. It would be interesting to see a large scale study of this to try to characterize when it's more likely to follow instructions/user preferences, and when it's greed for rewards gets the better of it!