Speaker unverified: uncertainty

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…deploy them. And the belief that actually they are uh the thing that really matters now. Yeah, and now it feels like, you know, the uh clearly the whole company priority, you know, is still locked in and focused on this. And you've seen just incredible improvement in in Codex in recent months. For all the developers who listen to the podcast, like if again, it it's almost like hard to comprehend like what the world looks like as these models keep whole climbing on longer and longer tasks. Like what do you think will look different in their lives? Or like how will they be using Codex in, you know, 3 6 months? I realize 3 months and 6 months are very different time lines in this world, but take a take whichever whatever in between point you'd like. I would expect just a gradual increase in just the level of autonomy you feel comfortable letting the model just the thickness of the description that's going to work with, you know, the level supervision it needs. I think we're not very far from models that can work autonomously for a couple days. Maybe just quite a bit more computer than we're using now and produce much higher quality artifacts on their own. Do you have a gut instinct on like what like, you know, there's always been this question of like will the world, you know, do you need that software engineering skill set to supervise these models running for a few days or like, hey, does it turn out at some point of like being able to run for a while, you know, anybody can can use coding agents and supervise them to to some sort of output? I mean, I think definitely for like a lot of outputs you already don't need that much experience, right? I think I think still the distinction I would draw between like, you know, an intern here and like really an autonomous researcher or software engineer would be that like if you want to build something bigger, like, you know, you probably probably still want to apply supervision. You still kind of want to have like an overarching thing. You want to recognize like what what what building blocks fit in and what which don't. But yeah, I definitely expect that like the desired skill set to shift quite a bit over time. Yeah. Towards towards this like more general vision setting. You know, I guess on on the on the research side I feel like there's been, you know, maybe maybe like a month ago I feel like all anyone could talk about was continual learning and there was just, you know, it was in the zeitgeist. There's all these neo labs starting to go focus on continual learning. Some folks left OpenAI to go focus on that. Um I'm curious like, you know, I I think it part maybe part behind that is a belief that like, you know, RL alone, you know, either won't get us there or will get us to like some level of very inefficient scaling and it's kind of different than the way, you know, humans learn. I think even I've heard you say before like that, you know, RL is still very different today than the way that humans learn. What's your take on on like that, you know, that whole movement? Yeah, I am a little bit confused by it because, you know, in my mind like the whole kind of like excitement that way that humans learn. What's your take on on like that, you know, that whole movement? Yeah, I am a little bit confused by it because, you know, in my mind like the whole kind of like excitement that like we've had, I mean, if if you look at the titles of like the GPT uh you know, three paper, right? Like it is that like oh, you know, this class of models is actually capable of continual learning, right? It's capable of like learning uh um learning to learn in context, right? That's has been really, you know, the driving force behind the kind of excitement to like scale these GPT models further. That has been like the premise for why we really need to teach them with RL so like learn in context more efficiently. And so, I definitely agree that continual learning is really the thing, right? Like it's really the thing that we're building, but I I don't really think this is like a problem that's like oh, you know, it's it's kind of ignorant and off the path of what we're doing currently. I think it is what we're working towards. >> Yeah, I think you're buying this is like the single best path to get there is to continue to kind of scale uh the pre-training in RL. I think that is kind of how we've made the most progress on this problem so far and you know, I think there are I I I I think that that there definitely are like more ideas, more steps. Um I think also a lot of improvement that will just come from scale. Yeah. And I guess like, you know, we have a lot of folks listening that maybe have, you know, have been able to do a lot of simpler things with these models and then they try to do like some of these more complex, you know, I don't know call it hundred step or longer term tasks and they're like oh, you know, the the the models don't work for this yet. And I think that's harder. You on the inside constantly feel this improvement, but for them it feels like hey, this is like night and day away from, you know, being able to do this much longer thing. How do you kind of articulate to them, I guess, the set of things that need to be true for these like much longer steps to happen? Is it around kind of checking in more often as you were talking about before or I feel like there's just this belief among the research community of like oh, all of these tasks will be solved in the next year or two and then in the wild a lot of people maybe not totally grokking that like improvement line that we've been seeing. Yeah, I mean I definitely a lot of that prediction comes from just looking at like historical improvement lines, right? And I think increasingly we can we can roughly see the the the the the shape here. I I don't think a lot of this is about just the models becoming intelligent enough to recognize like whether, you know, they're making progress. Um I think some of this is like we have this very kind of pragmatic work of like are the models actually you know, can they actually access, you know, all the contexts, all the files, all the infrastructure they need to do the work you want them to do which yeah, I I remember like in the past when we were discussing, you know, the kind of the the road map uh that we're taking with RL. You know, I ructure they need to do the work you want them to do which yeah, I I remember like in the past when we were discussing, you know, the kind of the the road map uh that we're taking with RL. You know, I definitely feel like, okay, we just need to teach the models kind of reason with its own tokens as kind of a priority and then of course we'll need it to use tools like interact with the environment. You know, at some point we definitely teach it to see, right? At some point we need to teach it to use a physical body, right? Like but like uh yeah, I mean I I think we're definitely like well into the stage where you know, it really needs to like interact with the environment and really needs to see uh and you know, someday soon we'll we'll really come up with robots, but Yeah, I mean it does feel like a lot of the times when I hear people complain about how a model can't do X or Y, it's like literally just cuz you haven't fed, you know, or connected to the systems or fed enough context into it. Actually, I do wonder if like context was universally applicable and able to flow into these things, like I feel like a lot of these problems would actually just be solved with today's models. You know, I want to talk about some of the AI for science stuff um that you guys have been working on and one thing in particular, you know, I feel like the coding stuff is something that everyone feels very viscerally um cuz you know, in every company they're using these tools and getting tons of productivity. You know, the math side, not all of us have competed in in in IMO competitions and uh necessarily have as much of like an intuitive feel for some of these breakthroughs. And so, one of them I know that was really interesting that you guys did is use some compelling work for around like first proof, right? And I think these are like very different problems than kind of traditional competition math. I want to have you just speak a little bit to that cuz I think it's just a space that our listeners might be less familiar with and kind of less familiar with understanding the implications of models being able to do pretty cool work here. Yeah, I mean, you know, I I I think yeah, I I was very excited about the first proof challenge and you know, I got I kind of because that particular one is kind of a benchmark, right? It's like a couple, you know, respected mathematicians, theoretical computer scientists releasing problems that like they believe are like representative of their day-to-day work, but haven't been published anywhere so that we can really have our models take a crack. We were so excited about this challenge, but you know, it was kind of dropped um without any any any any advanced warning. Um with like a week-long deadline to actually execute. Um we had a we had a very exciting model training at the time and so uh um uh um one of the people in charge of training, James Lee, kind of started prompting the uh that model just uh by hand and and and uh and yeah, and actually having seen oh, okay, oh, it's actually solving these problems. It's really really a fascinating thing to see. Uh you know, one of these problems actually is from a domain that I I I I…

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