The speaker says ORLMs and other harness abstractions differ substantially from most current harnesses, which generally look similar.

“with orlms and and with other harness abstractions it looks very different and this is where i think you really distinguish i mean it's it's in the same way that like i think with language model architecture choices a lot of architecture choices end up kind of looking the same when you like scale it out or like the differences end up being like somewhat minor in terms of i mean for a lab it's not minor but”

“you know maybe one model converges better than the other one like slightly but in general like if you were so for example pre-training scaling laws only hold because the architecture choices we have are somewhat stable right now but if you were to completely change the architecture pre-training scaling laws probably don't hold or like these kinds of this like power law is going to look very different and it's like the same thing with harnesses like i think all the harnesses we have right now for the most part”

“are roughly look the same,”

“but there are some exceptions to this, I think, that are coming out.”