I think both more reasoning expenditure (limited, diminishing returns) and more competent/​informed base pretrained model can make RL more sample-efficient, by making successful rollouts more likely to begin with. (You pay less exploration tax.)
Similarly, well-designed curricula or adaptive sourcing/​selection of demonstrations could make RL more sample efficient.
I think both more reasoning expenditure (limited, diminishing returns) and more competent/​informed base pretrained model can make RL more sample-efficient, by making successful rollouts more likely to begin with. (You pay less exploration tax.)
Similarly, well-designed curricula or adaptive sourcing/​selection of demonstrations could make RL more sample efficient.