One factor that is going to limit this reward-maxxing behavior is how achievable the rewards are, and how countable they are.

For example, if you ask the model to do 10 relatively easily achievable things (pass these 10 test cases), then if/when it completes them it will probably stop (unless maybe it invents it's own stretch goals - you never know!).

OTOH, if you gave the model a list of 10 goals that turn out to be impossible, or extremely difficult, maybe together with encouragement to be relentless, then there is much more chance that it may do something unexpected having failed on all the more obvious approaches.

Similarly, if you give the model an open ended goal such as "make as many paperclips as you can!", then it may start with the easier and more predictable methods, but with no defined stopping criteria it may just continue ...