So to really start solving this problem, we have to turn something that feels fuzzy like if I ask you what is great design into something that is more verifiable. So there's a few questions here, right? Because if I ask you this of what is great design um you could ask yourself, okay, um do you mean great for which type of person, for which type of taste, for which situation? The same slide could be amazing. for example, if you are a startup and completely inappropriate if you are a finance firm. So it's contextual first of all. Second of all, it has this property that it changes over time which is different from other domains. What is considered good today is different than five years ago and different than five years from now. In code that's not necessarily true or in math, right? That's something that is way more consistent over time. So our ability to again decompose it and understand how is this good for a specific audience, how is this good today, how is this good in context um is some of the things that we've been thinking about in terms of how to how to break this down. But I want to give you a very specific example because of course this can mean many things. So let's talk about brand. Um if you're at a company and you've used coding agents, you've probably shipped an internal dashboard. You've probably shipped an internal like landing page. And oftentimes you might wonder, okay, how do I determine if this is slop, if this is actually good? And you have kind of this secret weapon at your disposal, which is really all the work that probably designers at your companies, for example, put into defining your brand. A brand to define takes a lot of effort, takes a lot of care. You're defining all these components about it, when it's good, why you're choosing certain combinations of colors, of typography, of spacing, of texture. Um, but if I just ask you to be like, okay, create something great, that's very hard. But suddenly if I'm like okay make something that is on brand that is a much easier problem to define and a brand is something that can become decomposable. Uh so for example if we I'm using the reduct brand as an example here because I I I like their website. Um let's say that we decompose this brand into the colors, the typography, the motion, the animation, the textures. Suddenly you have these very codified things that you can verify against. Verifying in general if something's on brand and you can try this uh by prompting an LLM as a judge to do it is quite hard. But once you start picking apart the exact elements that represent what great is, then it suddenly becomes the shape of something that is codifiable and verifiable. So if you want to turn this into a shape of an RL environment, for example, right, how would you train a model for a capability like brand adurance? Uh LLM LLM as a judge might not necessarily always be the best method. We know that there's a lot of reward hacking. We know that there's uh interesting hallucination patterns there too. And so we oftentimes try to create methods of basically how do we turn a task that feels fuzzy into one where there's a clear ground truth so that it can become the shape of an environment. So in this case the task design itself is really kind of the hardest part of the problem of how do you turn something that appears very fuzzy into something that actually can be arled. Um and so in this case that decomposition that I mentioned becomes the ground truth. So let's say that you start by tasking an agent to create a new page that is going to adhere to the reductal brand but be completely net new and different. Um you would want that output to not only be graded versus the original but to be graded on this ground truth, right? Because it could come up with completely new ways of using these components that are still valid but are different from the original. So you don't necessarily want to just see if it's replicating the original. So this is one example of like how to turn this into a problem of environment shaped um so that we can make it more verifiable. But in a way all of these things are I would say like spectrums right you have uh in a problem like design you have these elements of things like vision uh alignment typography that are closer to objective once you start moving up that scale onto things like style fit creativity how do you judge and measure something like creativity right that's much harder and so you kind of need to think of this as like a routing problem of how do you understand this like vast fuzzy problem break it down into smaller components and what is the best solution for each of these components. So why does something feel like slop? For example, when we're talking about something like creativity, right? I think this is the second reason why um subductive domains are so much harder to solve because if we're talking about the properties of models, they're basically predicting what's the most likely outcome to show up next. And they assume that that outcome is the ideal outcome. And for something like math encoding, that is true, right? You want the answer that your model gives you to be the average answer. If you're asking what 2 plus 2 is, uh, which also happens to be the right answer and the optimal answer. But for something like writing or design, you don't necessarily want the average answer, right? The average meaning the most likely does not necessarily coincide with like the optimal. Uh, a lot of like what I describe it is a lot of greatness and creativity happens actually at the ends of the distribution. It's not the most likely outcome. It's when you actually actively break from rules and actively break from patterns that you can create things that are subjective and and great. Um, and so the reason why this feels like slop and that we have this feeling that we're surrounded by by slop is exactly because of this collapse to the mean and this repetition. And so we have to find ways of okay, how do we break these patterns? How do we break from the mean uh but in a way that's also intentional. So then you kind of shift the problem onto things that are not so easily maybe verifiable. Uh but that are more questions of human preference and judgment and that might be better solved by data for example than by environments. And so again this kind of like mode collapse is is really the thing that we're trying to solve. And how do we force that distribution back? Uh, oftentimes, by the way, we we work, for example, with a community of designers, um, over like a thousand experts that are experts in different types of medium, different styles, and we purposely want to force that distribution when we're breaking down the problem. Exactly. So, we don't end up in this mode collapse, but there's of course a lot of other pieces of that puzzle. Uh, but I think this is an interesting framework is like the closer you are to something that it becomes verifiable, especially programmatically, the better for something like RL, right? And I think the challenge is how do we turn things that feel fuzzy into things that become more verifiable by establishing this ground truth and designing tasks in a way that allow for that. But then the more that it does shift to things that are contextual or that depend on that time element that we talked about or this like distinction in preference, the more this shifts towards something that requires human judgment. So uh I I I like this analogy of basically kind of pulling things toward verification. So brand adherance on its own would be something that's very hard and that is probably better judged by a human than for example by an LLM as a judge or something deterministic. But by codifying it by understanding which pieces matter and how do I turn that into something that is observable and measurable we can kind of pull it into this realm of verification. uh so this routing logic is I would say if you have