The speaker considers RFT highly sample-efficient because each example can yield dozens of labels and trajectories, allowing more signal to be extracted from a dataset.

“you get points right and I think that's what RFT brings to the table it's like it gives you a way to optimize for the end objective and ambience cares about that in healthcare and the second I think is like it's it's tremendously sample efficient right each example sort of blooms into dozens of labels right and trajectories you can squeeze like x more signal right out of out of a data set I think those two things are like tremendously profound I'll pause there but yeah there's some cool stuff”