The speaker says pre-training produces a smooth-looking loss curve while models learn many discrete capabilities, some of which emerge through phase transitions, such as the appearance of induction heads.

“quanta so you have this very smooth like pre-training Moscow and when you actually look at what's happening in the model like the model is learning all these like very discrete like tasks and there's like always like emergent points where there's like kind of a phase transition like it didn't have induction heads now it has induction heads and there's like tens of thousands millions probably like hundreds of millions of these things and you average them all together and you get this very like smooth loss curve I think like to us to an extent like a similar thing is happening happening for RL”