Beam has 500 billion total parameters and 23 billion active parameters.

“per training flop is is increasing so at some point that asymptote is okay and then also like the amount of revenue that each flop can generate also increases right the more you pack intelligence so going back to the question around how does this distribute across pre-training and reinforcement learning and to train beam which is a 500 billion parameter model total 23b active it was”