The speaker says that more reinforcement-learning rollouts do not necessarily improve a model and that practical constraints can prevent a lab from using all its available compute for training.

“um rl you know generating even more rollouts during rl does not necessarily make it better there's all sorts of reasons why you may not be able to leverage you know all two gigawatts that you have for training onto training right actually i can only leverage 200 megawatts as we get closer and as we get further and further down implementate automated coding automated researcher i actually”