reasoning models

“The question of like alignment is actually more important for this like complex reasoning models to like, how do we help humans to like verify the outputs of these models is quite important.”

“But as with text agents, we've really found that we made a jump on the S curve with reasoning models. I think it's going to be the same with the next visual models, basically better base models just allow you to jump over this S curve. And probably I think it'…”

“And like, it feels like, again, like back to this, the thread around these capabilities, like it feels like long context with reasoning is like finally going to be that thing where like, it actually just like blows the lid off of it. And like, it makes the use…”

“You need evals to train your reasoning models.”

“I do think reasoning models for planning and using kind of more targeted models to execute is definitely a good architecture.”

“Basically, where GPT, 4.1, can it kind of explore, go through a repo? It's been trained to do that particularly well. Whereas you know, to just get some code and produce a change, a reasoning model might do better because it can kind of reason over the entire …”

“No, that's reinforcement fine tuning is only for reasoning models.”

“One thing we have found in preliminary results is that the reasoning models don't seem to do as well as you'd expect in this setting. And I think that's probably because the way we set this up, it's a bit like we're already making it do reasoning traces over a…”

“And the most, one of the most promising ways, I think, towards doing this is having the reward models also be able to answer harder questions by themselves being reasoning models.”

“The thing that's going to make the next wave of stuff be powerful is just, like, everyone wants better agents. Everyone wants models that can, like, go off and do stuff. And, like, reasoning was kind of, like, a precursor to that a little bit.”

“And that is very clearly a domain where you don't have an easily verifiable metric for success. It's very like, what is the best research report that you could generate? And yet these models are doing extremely well at this domain. So I think that's like an ex…”

“I think that has to be the north star for most people working on reasoning, which is the model will just Spend the right amount of tokens on it.”

“I definitely don't think the algorithm tends to be the most important thing.”

“I have a theory also that reasoning models have better context utilization because they can loop back. Normal auto-aggressive models, they just kind of go left to right, but reasoning models, in theory, they can loop back and look for things that they needed c…”

“No amount of workflows will suffice for a big enterprise. Like you have to link together some of the missing pieces, some of the poorly instrumented data, and that requires world knowledge and a few iterations with the world knowledge.”

“Most enterprises don't really want to use reasoning models. The latencies is too high”

“So, earlier this year, and probably when you and George last spoke for the AI engineers world's fair, we had this great slide that was super easy, where we would show that the average reasoning model is using 10 times the number of tokens per query in our inte…”

“And this I think is like the fundamental difference between reasoning models and the four ones and four O's of the past where these models could not think. So you kind of had to put them in boxes with a predefined set of state transitions. Whereas here we have…”