The speaker describes the model's generalization as uneven: it adapts quickly to a task when given suitable data, but does not generalize cleanly across different benchmark harnesses.

“At the same time, it is somewhat jagged where it quickly adapts to data once you have the right data for, you know, tasks you want to do. But there is kind of a jaggedness about it. And it's even between like the various benchmarks like”

“the various versions of terminal bench like you don't see clean generalization between different harnesses but once you have something working you have a generally gigantic capability and you get the data for a new harness it actually starts adapting to it pretty quickly so what that means is that you have this like pretty adaptive thing”