Chapter 1: What is the main topic discussed in this episode?
Welcome, humans, to the Neuron AI Explained. I'm your host, Corey Knowles, and I'm here as always alongside us with a spring in his step and a button as hard as our own Grant Harvey. How are you today, my friend?
I'm doing well. I'm doing well. I love that. That's a very visual flowery description of me, which I welcomely embrace. How are you? Well, what are we talking about today, Grant? Today, we are talking about reinforcement learning environments, the training grounds where AI agents learn to plan, use tools, adopt, and make judgment calls across multi-step tasks.
These environments are quickly becoming the bottleneck for real-world AI performance. And in 2025, they were one of the most aggressively funded and least understood parts of the AI stack.
Our guest today is Nick Heiner, head of RL Environments at Surge AI, where he leads the company's reinforcement learning environments team. Before Surge, Nick was a founding engineer at Fixie, a senior engineer at Netflix working on UI platforms, and part of the U.S. Digital Service.
At Surge, he's helped build large-scale simulated workplaces like CoreCraft, environments used by frontier labs to test whether models can actually do knowledge work end-to-end.
He's also the co-author of Surge's recent research showing that even the best models fail roughly 40% of the time on real workplace tasks, with failures clustering around planning, adaptability, groundedness, and common sense.
Nick, welcome to The Neuron. Glad to have you.
Thank you. Happy to be here. You mentioned that it's one of the best funded but also least understood areas right now.
Do you agree with that assessment?
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Chapter 2: What are reinforcement learning environments and why are they important?
Like we're just we're piling in here and there's still a lot to learn about the space.
Well, hopefully we can help out all of your VC friends and teach them a bit more direct from the horse's mouth here. I guess to start, how did you end up at Surge AI building RL environments? And what was the moment that you realized that this was the future of AI training?
So, I mean, the moment that I sort of left Netflix and went into AI startups in general was basically the moment, the first moment I used ChatGPT. And it was just, I'm sure everyone remembers where they were when they first used ChatGPT. I remember the moment. Right. It's just immediately obvious that this is something totally different.
And, you know, I love Netflix, but it just didn't feel like a time to be at a 25-year-old company in a well-established space. It's like this is a whole new field. So I came over to Surge, and at the time, everything was scaling up really fast.
Like, you know, Llama 2, GPT-3, sort of those initial models had come out, and everyone's looking at the scaling laws and saying, okay, now we just need to bump it up in order of magnitude. which means the entire supply chain needs to get bumped up in order of magnitude, of which we at Surge were a part. So when I first joined, I focused a lot on building out our expert network.
And that has a bunch of pieces to it. There's the actual recruiting, but then there's things like, how do you vet people? How do you see who's the best at what type of tasks? How do you apply quality checks to work at broader and broader scales? Then I transitioned to lead several of our client engagements. And that was during 2024.
And the big thing there was like, again, in 2023, a lot of these models were being produced by scrappy small bands of researchers at these labs. And then 2024, all those orgs scaled up from 20 geniuses to an org of 1000 people. And, you know, in much the same way, we had to scale up too.
And they sort of had new expectations of us, you know, high-level enterprise maturity, and then just being able to produce super high-quality data for, you know, 40 different research tracks at once instead of like a team of 20 that was focused on three. Wow. So that was a big part of my work in 2024 was sort of scaling my teams to do that.
And then 2025 focusing on our environments and a lot of the actual work, but a very strong through line with stuff that we had already been doing with labs. Um, some of it was sort of ties and pieces together. Um, some of it was just sort of a crystallization of a lot of other stuff we've been doing.
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Chapter 3: What challenges do AI models face in real workplace tasks?
Then there's reinforcement learning from human feedback, which is when you golf, you know, you have an instructor, you're the driving range, you take two shots and the coach tells you, OK, the first one was better. And they don't necessarily even tell you what was better about it. They just tell you one was better than the other.
And you like you sort of try slightly different things every time and you start to converge on like what is the best thing to do. And then reinforcement learning environments takes it a step further.
And so instead of you're the driving range and you're limited by the availability of the coach, which, you know, to sort of say what it actually is, it's like you have humans looking at two responses from a model and choosing, you know, thumbs up, thumbs down. But that requires humans, right? Like you have to spend millions of hours to do that.
The reinforcement learning environment is you're sent out in the golf course by yourself. And you get feedback from the environment of like, okay, the ball went close to the target. Right. And in that way, you're able, again, to sort of self-teach in a sense, because you keep trying different things and then you keep getting that feedback of what worked and what didn't.
And yeah, you do that for a million hours and then all of a sudden you're a world-class golfer.
Makes me think of the thumbs up, thumbs down. Do you like this personality button on chat GPT? I see all the time.
Yes. And that is exactly what they're doing is they are collecting your user feedback. And so we've it's actually somewhat funny. You know, we've had experts in our network who spend a lot of time, you know, going in a lot of detail into these responses to assess which ones are better and they get paid to do it.
And when they see ChatGPT asking for that same information for free, like some of them have actually complained, you know, sort of in like, I mean, not like, I mean, they're just sort of vetting. It's not a serious thing, but yeah. But yes, but that is exactly what they're doing is they're gathering training data.
Okay. That's good to know. Well, the RL example with you're going out onto the golf course and you're trying to, based on the feedback that you get, adjust your game. That just, to me, feels like the most similar to how we humans learn in general. Do you agree with that?
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Chapter 4: How does Nick Heiner's experience shape his views on AI training?
And then it will faithfully write the rest of that document. And so post-training is where you teach it not to do that.
Yeah.
Got it.
Kind of the behavior end.
Yes, exactly.
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Learn more today at dell.com slash yourwaytoai. That's dell.com slash yourwaytoai. So what makes a good environment versus just writing test cases? What are the differences in a good and bad environment?
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Chapter 5: What factors contribute to the effectiveness of RL environments?
Nope.
Right. Right. Exactly. You know, every, every like longitudinal nutrition study. Right.
But, you know, but just, just like in biology where people are working on cell simulators to be able to get around some of that, you know, that is, again, the benefit of an oral environment is once you figure out how to verify these tasks, you have a simulated environment in which you can, you know, have many hours, you know, many years of like,
simulation time compressed into a much shorter walk lock time and then try to get some of that signal okay this brings me to actually one of my biggest questions which is um which kind of came up in conversation here uh are you doing any testing with like multi-agent um swarms like i know this is kind of like the big hot topic you know open claws talking about this a lot
There's even Molt's book, which is like all of the agents talking to each other. Like, would you ever simulate, you know, would you try to simulate, you know, a thousand people reading a textbook and, you know, how, you know, how they actually react to it? And I have a follow up question on that, but I'll just let you react.
Yeah, I mean, it's like the Codex app that just recently came out where they're really having as a first class concern managing a swarm of agents. So yeah, I mean, the simpler form is like what you see we published already with CoreCraft where you have one agent solving a customer support task at a time.
But yeah, when you get more nuanced, it's things like a financial markets simulator where you have multiple agents that are all participating in the market at the same time and in real time. And you just see sort of who comes out on top. So, yeah, that's definitely a very interesting area of research for us.
Related to that, do you have any sort of intuition or suspicion on whether or not the model that will be able to generalize across all of these different domains and be able to do a deep analysis and, you know, end a PowerPoint presentation? Will that be a single model or will that be like five models that are all work together? What's your take?
Yeah. Yeah. So it's important to distinguish here. Like when people say multiple models, sometimes they literally mean like a new model has been trained to make PowerPoints. And sometimes they mean it's the same LLM that just has like a different system prompt. It's just sort of like an agent that's pointed towards a different subtask. Right. Yeah.
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Chapter 6: What is the significance of reward signals in AI training?
So so one thing we found, as you'll note in the write up, like we had said, a lot of the models behaved as if they were solving an academic problem. And this is interesting, but actually not surprising at all, because, you know, again, you are your objective function, right? Like you get what you're trained for. And a lot of benchmarks are fairly academic and contrived.
And this is a natural consequence of the fact that building a benchmark is incredibly expensive. And a lot of them are being done from an academic context that don't have huge budgets. And so like, you know, if you imagine like some of the questions that we were posing to the models here, they take a finance professional 20, 30, 40 hours to do.
So in order to build a benchmark, hundreds of questions like that, you need to find enough finance professionals and you need to pay them to spend 20 to 30, 40 hours per task. And so that's quite a lot. And frankly, until you've made an investment in having a really deep expert network and a lot of technology to produce great data with those people, it's just not feasible.
And so that's why you see a lot of benchmarks that have been used are like glorified like SATs. And so, you know, that's why we see that the models sort of behave in a very academic way. But when you put these real world constraints on them, like they're sort of that last mile problem.
You know, I'll give you another example of this, which is many coding agents do not want to use external libraries unless you really force them to do so. When you say I have a bunch of like, you know, chess puzzles and I want you to write a program is going to solve them. The obvious thing to do is to get Stockfish, just write a little wrapper, and then spit that out.
If you're saying this is for a production system, I'm building a chess app, yeah, just get Stockfish. Models want to make their own chess solver. Yeah, they want to build it from scratch. Because that's what you would do in an academic setting. You're not being tested on your ability to use Stockfish. You know, it's like those memes, you know, guys will do anything to avoid going to therapy.
It's like, you know, similarly, it's like coding models will do anything to avoid using the already perfectly good available library.
But I will say I kind of respect that with a situation like NPM where, you know, it just got hacked with Shilud. And I almost now I'm like, I don't want to use anything with NPM, you know, as an amateur vibe coder. Yeah, for sure.
And I do think it's a great feature of these models that we will be perhaps reaching for left pad less frequently because you can just have the confidence that your model can write that 10 line function for you.
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Chapter 7: How do reinforcement learning environments differ from traditional training methods?
But there's a tie back to finance. Like, yeah, what we see is like excellence on sort of the core thing you would do in school versus But then when there are a lot of details or especially when the task is structured, not as I've given you everything in the prompt, all neatly bundled up.
But you need to go into our environment and like search through our confluence to find like our standard procedure for how we model this scenario. Check your email from a note for the VP that said, you know, here's some other important context for how this analysis needs to be done. You know, we got five different CSVs from the client. One of them is the relevant one.
The other four are out of date now. Like when you add all that stuff in, you know, that's where the models tend to fall apart.
Yeah.
That makes sense. So I guess let's talk a little bit about, you know, kind of the business end here. Should companies be investing in building their own RL environments and training their own models? Or is that still a waste of resources in a lot of ways and maybe best left to Frontier Labs?
Yeah, so I will admit that, you know, I work at a company that produces aerial environments. So if you're asking me, you know, I'm going to have a certain perspective here. But I think my perspective is also correct. And now I will share it. So, you know, I think it really comes down to... what the factory, your previous guest, the factory CTO, I think, was saying. Eno, yeah. Eno, yeah, yeah.
Where he was saying that making the code base agent-ready has a huge impact. And that absolutely matches what we've seen in our own research.
Yeah.
And so I don't think companies want to be building their own RL environments because there's a lot of work and infrastructure that goes into that that is not really part of the core competency. what they should be doing is making their entire business agent ready. And so that basically means that like,
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