Arcee AI goes all-in on open models built in the U.S.

Arcee AI is a the startup I’ve found to be taking the most real approach to monetizing their open models. With a bunch of experience (and revenue) in the past in post-training open models for specific customer domains, they realized they needed to both prove themselves and fill a niche by pretraining larger, higher performance open models built in the U.S.A. They’re a group of people that are most eagerly answering my call to action for The ATOM Project, and I’ve quickly become friends with them.

Today, they’re releasing their flagship model — Trinity Large — as the culmination of this pivot. In anticipation of this release, I sat down with their CEO Mark McQuade, CTO Lucas Atkins, and pretraining lead, Varun Singh, to have a wide ranging conversation on:

* The state (and future) of open vs. closed models,

* The business of selling open models for on-prem deployments,

* The story of Arcee AI & going “all-in” on this training run,

* The ATOM project,

* Building frontier model training teams in 6 months,

* and other great topics. I really loved this one, and think you well too.

The blog post linked above and technical report have many great details on training the model that I’m still digging into. One of the great things Arcee has been doing is releasing “true base models,” which don’t contain any SFT data or learning rate annealing. The Trinity Large model, an MoE with 400B total and 13B active tokens trained to 17 trillion tokens is the first publicly shared training run at this scale on B300 Nvidia Blackwell machines.

As a preview, they shared the scores for the underway reasoning model relative to the who’s-who of today’s open models. It’s a big step for open models built in the U.S. to scale up like this.

I won’t spoil all the details, so you still listen to the podcast, but their section of the blogpost on cost sets the tone well for the podcast, which is a very frank discussion on how and why to build open models:

When we started this run, we had never pretrained anything remotely like this before.

There was no guarantee this would work. Not the modeling, not the data, not the training itself, not the operational part where you wake up, and a job that costs real money is in a bad state, and you have to decide whether to restart or try to rescue it.

All in—compute, salaries, data, storage, ops—we pulled off this entire effort for $20 million. 4 Models got us here in 6 months.

That number is big for us. It’s also small compared to what frontier labs spend just to keep the lights on. We don’t have infinite retries.

Once I post this, I’m going to dive right into trying the model, and I’m curious what you find too.

Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.

Guests

Lucas Atkins —X,LinkedIn — CTO; leads pretraining/architecture, wrote the Trinity Manifesto.

Mark McQuade — X, LinkedIn — Founder/CEO; previously at Hugging Face (monetization), Roboflow. Focused on shipping enterprise-grade open-weight models + tooling.

Varun Singh — LinkedIn — pretraining lead.

Most of this interview is conducted with Lucas, but Mark and Varun make great additions at the right times.

Links

Core:

* Trinity Large (400B total, 13B active) collection, blog post. Instruct model today, reasoning models soon.

* Trinity Mini, 26B total 3B active (base, including releasing pre-anneal checkpoint)

* Trinity Nano Preview, 6B total 1B active (base)

* Open Source Catalog: https://www.arcee.ai/open-source-catalog

* API Docs and Playground (demo)

* Socials: GitHub, Hugging Face, X, LinkedIn, YouTube

Trinity Models:

* Trinity models page: https://www.arcee.ai/trinity

* The Trinity Manifesto (I recommend you read it): https://www.arcee.ai/blog/the-trinity-manifesto

* Trinity HF collection — (Trinity Mini & Trinity Nano Preview)

Older models:

* AFM-4.5B (and base model) — their first open, pretrained in-house model (blog post).

* Five open-weights models (blog): three production models previously exclusive to their SaaS platform plus two research models, released as they shifted focus to AFM — Arcee-SuperNova-v1, Virtuoso-Large, Caller, GLM-4-32B-Base-32K, Homunculus

Open source tools:

* MergeKit — model merging toolkit (LGPL license return)

* DistillKit — knowledge distillation library

* EvolKit — synthetic data generation via evolutionary methods

Related:

* Datology case study w/ Arcee

Chapters

* 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large

* 00:08:26 Transitioning a Company to Pre-training

* 00:13:00 Technical Decisions: Muon and MoE

* 00:18:41 Scaling and MoE Training Pain

* 00:23:14 Post-training and RL Strategies

* 00:28:09 Team Structure and Data Scaling

* 00:31:31 The Trinity Manifesto: US Open Weights

* 00:42:31 Specialized Models and Distillation

* 00:47:12 Infrastructure and Hosting 400B

* 00:50:53 Open Source as a Business Moat

* 00:56:31 Predictions: Best Model in 2026

* 01:02:29 Lightning Round & Conclusions

Transcript

Transcript generated with ElevenLabs Scribe v2 and cleaned with Claude Code with Opus 4.5.

00:00:06 Nathan Lambert: I’m here with the Arcee AI team. I personally have become a bit of a fan of Arcee, ‘cause I think what they’re doing in trying to build a company around building open models is a valiant and very reasonable way to do this, ‘cause nobody really has a good business plan for open models, and you just gotta try to figure it out, and you gotta build better models over time. And like open-source software, building in public, I think, is the best way to do this. So this kind of gives you the wheels to get the, um... You get to hit the ground running on whatever you’re doing. And this week, they’re launching their biggest model to date, which I’m very excited to see more kind of large-scale MoE open models. I think we’ve seen, I don’t know, at least ten of these from different providers from China last year, and it’s obviously a thing that’s gonna be international, and a lot of people building models, and the US kind of, for whatever reason, has fewer people building, um, open models here. And I think that wherever people are building models, they can stand on the quality of the work. But whatever. I’ll stop rambling. I’ve got Lucas, Mark, um, Varun on the, on the phone here. I’ve known some of them, and I consider us friends. We’re gonna kind of talk through this model, talk through building open models in the US, so thanks for hopping on the pod.

00:01:16 Mark McQuade: Thanks for having us.

00:01:18 Lucas Atkins: Yeah, yeah. Thanks for having us. Excited.

00:01:20 Varun Singh: Nice to be here.

00:01:20 Nathan Lambert: What- what should people know about this Trinity Large? What’s the actual name of this model? Like, how stoked are you?

00:01:29 Lucas Atkins: So to- yeah.

00:01:29 Nathan Lambert: Like, are you, like, finally made it?

00:01:32 Lucas Atkins: Uh, you know, we’re recording this a little bit before release, so it’s still like, you know, getting everything buttoned up, and inference going at that size is always a challenge, but we’re-- This has been, like, a six-month sprint since we released our first dense model, which is 4.5B, uh, in, in July of last year, 2025. So, um, it’s always been in service of releasing large. I- it’s a 400B, um, thirteen billion active sparse MoE, and, uh, yeah, we’re, we’re super excited. This has just been the entire thing the company’s focused on the last six months, so really nice to have kind of the fruits of that, uh, start to, start to be used by the people that you’re building it for.

00:02:16 Nathan Lambert: Yeah, I would say, like, the realistic question: do you think this is landing in the ballpark of the models in the last six months? Like, that has to be what you shop for, is there’s a high bar- ... of open models out there and, like, on what you’re targeting. Do you feel like these hit these, and somebody that’s familiar, or like MiniMax is, like, two thirty total, something less. I, I don’t know what it is. It’s like ten to twenty B active, probably. Um, you have DeepSeeks in the six hundred range, and then you have Kimi at the one trillion range. So this is still, like, actually on the smaller side of some of the big MoEs- ... that people know, which is, like, freaking crazy, especially you said 13B active. It’s, like- ... very high on the sparsity side. So I don’t actually know how you think about comparing it among those. I was realizing that MiniMax is smaller, doing some data analysis. So I think that it’s like, actually, the comparison might be a little bit too forced, where you just have to make something that is good and figure out if people use it.

00:03:06 Lucas Atkins: Yeah, I mean, if, if from raw compute, we’re, we’re roughly in the middle of MiniMax and then GLM 4.5, as far as, like, size. Right, GLM’s, like, three eighty, I believe, and, and thirty-four active. Um, so it-- you know, we go a little bit higher on the total, but we, we cut the, uh, the active in half. Um, it was definitely tricky when we decided we wanted to do this. Again, it was July when... It, it was July when we released, uh, the dense model, and then we immediately knew we wanted to kind of go, go for a really big one, and the, the tricky thing with that is knowing that it’s gonna take six months. You, you can’t really be tr-- you can’t be building the model to be competitive when you started designing it, because, you know, that, obviously, a lot happens in this industry in six months. So, um, when we threw out pre-training and, and a lot of our targets were the GLM 4.5 base model, um, because 4.6 and 4.7 have been, you know, post-training on top of that. Um, and, like, in performance-wise, it’s well within where we want it to be. Um, it’s gonna be... Technically, we’re calling it Trinity Large Preview because we just have a whole month of extra RL that we want to do. Um- But-

00:04:29 Nathan Lambert: I’ve been, I’ve been there.

00:04:31 Lucas Atkins: Yeah, yeah. But i- you know, we’re, we’re in the, um, you know, mid-eighties on AIME 2025, uh, GPQA Diamonds, uh, seventy-five, um, at least with the checkpoint we’re working with right now. We’re still doing more RL on it, but, um, you know, MMLU Pro, uh, eighty-two. So we’re, we’re, we’re happy. We’re really-- Like, for it being our first big run, like, just getting it trained was, was an extreme accomplishment, but then for it to actually be, like, a, a genuinely useful model is a, a cherry on top.

00:05:03 Nathan Lambert: Yeah, let’s go big picture. Uh, like, let’s recap. We have all of the... We have this full trinity of models. I think that there’s a fun note. Uh, did I put it in this doc? Yeah, on Nano Preview, which was the smallest- ... you’re, like, charming and unstable. The model card’s really funny. Um, ChatGPT, doing deep research on this, I was like, ChatGPT Pro just tagged next to it, “charming and unstable.” And I was like: Is this a hallucination? And then in the model card, you have, like: “This is a chat-tuned model with a delightful personality and charm we think users will love. Uh, we think- ... it’s pushing the boundaries, eight hundred million, um, active parameter, and as such, may be unstable in certain use cases.” This is at the smallest scale- ... which is like, I appreciate saying it as it is, and that’ll come up multiple times in the conversation. And then you have Mini, which is like, um, I think it was, like, 1B active, 6B total type thing. In my-- I, I don’t have it, the numbers right in front of me. I have it somewhere else. Um-

00:05:52 Lucas Atkins: Yeah, Nano was, Nano was the 6B, uh, 1 active.

00:05:55 Nathan Lambert: Oh, yeah, yeah.

00:05:55 Lucas Atkins: And then, and the Mini was twenty-six, 3B active.

00:05:58 Nathan Lambert: Yeah. So, like-

00:06:00 Lucas Atkins: Um, yeah.

00:06:00 Nathan Lambert: -are these based on more of, like, you need to build out your training chops, or are you trying to fill needs that you’ve-... heard from community, and like, I think for context, previously, your first open model was a base and post-trained model, which was Arcee 4.5B, which was a dense model- -which people like. And prior to that, you had, like, a long list of, like, post-training fine tunes that you had released. So before that, it was like a post-training shop, and I think that kind of history is i- important to fill in, ‘cause I think most people-- a lot of people are gonna meet you for the first time listening to this.

00:06:34 Lucas Atkins: Yeah, it, it, um, we chose those sizes for Mini and Nano, uh, specifically Mini, um, the 26B, 3B Active, because we wanted to de-risk, uh, large. Like, th- this has all been in service of getting to a model of, of, you know, the 400B class. So, um, we, you know, learned from doing the original 4.5B, that you might have everything on paper that you need to train a model, but i- inevitably, there’s tremendous, you know, difficulties that come up, and, um, it, it’s-- we, we definitely knew we wanted to make sure that we, you know, solved some of... E- especially when it came to just doing an MoE model performance, uh, you know, like a, like an efficient, fast train of an MoE. So, um, we thought that that was a good ground where we could, you know, it wasn’t crazy expensive, uh, but gave us a lot of data, uh, going into large. And then Nano just came about because we had some extra compute time, and we really want to do more research on, like, smaller models that are very deep. Um, and we hadn’t really seen that in an MoE before, so that one was very much we started training it, and then it, you know, early benchmarks were good, so we said, “Well, we’ll just do the whole dataset.” Um, and, uh, but most of the love for those releases went into, to Mini. So I, I definitely think that long term, uh, from an ROI perspective, the smaller models are going to be where we shine, just because there’s a tremendous amount of, of cost savings a company can get from, from optimizing on a, on a smaller model. Um, but, but we, uh, w- we’re definitely gonna be trying to push the, the large frontier, too.

00:08:26 Nathan Lambert: Yeah. Um, I’d like to kind of double-click on training before going back to the small model that’s useful for companies, ‘cause we’re gonna have-- we’re gonna end up talking for, like, twenty minutes plus about open ecosystem. So I kind of am curious, like, philosophically, how your company feels about, like, sharing scientific details. So if I ask you, like, what are the things you’re technically most excited about in the model, or, like, what are the pain points? Like, uh, like, are you willing to talk about these things? Like, I- Do you feel like it’s kind of orthogonal to the company? Like, I feel like a lot of it is just, like, things that happen. I think your framing of all of this is in service of getting the big model going. And particularly, of, like, you have to be thinking about your model as landing in six months, is probably... Like, for people not training models, it’s hard to think about, ‘cause even I- ... like, I’m thinking about trying to refresh our post-training stack for OLMo 3, and I’m like, the thinking model, the, um, we are pretty SFT heavy right now, and it makes it not very dynamic in terms of the thinking time. But it’s just like, I can’t see people deploying this model, or probably will have a hard time fine-tuning it. And it’s like to think about where tool use models are going in six months, like, seems pretty hard. Um, it’s a very hard task to do, so it takes a lot of gumption to actually set out and do it. So I, I would just appreciate the framing, kind of self-reflecting on what I go through. So if you have anything that you think was, like, particularly hard to actually land the six-month outlook, because you use Muon as an optimizer, or is it Muon? And some of these things. I think the data, it’s well known that Datology is cranking a lot of this, and you probably provide-- I think of it as like you’re kind of driving and working with these partners, and I’m sure you provide a lot of feedback on what’s working and what’s not. So- ... anything you’re willing to share, I think it’s useful.

00:10:08 Lucas Atkins: Uh, I, I think, um, I mean, on the data side, like Datology, I-- at least for these models, that, that partnership has very much been almost an extension of our own research team. Like, we’ve worked very closely with them, and, um, obviously, our model’s doing well, you know, i- is, is, is good for them. So, um, but it, it-- there was definitely, you know, and you know this better than most, like, small-scale ablations, when you throw them at scale, sometimes, you know, uh, the-- i- it doesn’t always turn out how you want. So there was quite a lot of iterating there to at least get the dataset we used for Large. Um, I, I would say that as far as looking out six months and then figuring out how we wanted to... Obviously, the big one was compute. We don’t, um, you know, we, we never raised as, like, a foundation model company, so we’ve ne- we haven’t signed massive commits for, you know, thousands of GPUs before. Um, we didn’t have a, a, a massive cluster that was always active, uh, for a lot of our post-training. So if they came before, um, you know, we had sixty-four, uh, H100s, that was pretty sufficient for that kind of work, but obviously, this necessitated quite a bit more. Um, but the first thing was-

00:11:29 Nathan Lambert: That’s still less than people would guess. Like, you’re releasing models- ... that weren’t like, your models weren’t catching national news, but people in the community knew about them. And, like, uh, i- I think of, like, Moondream when I think about that. Like, vik has- ... such little compute, and he puts it to so use. Like, you, like, see how successful he is? And he tells you that he has, I don’t know, thirty... Like, l- it might be, like, sixty-four GPUs. Like, uh- ... there’s, uh, uh, that’s a whole separate conversation on building- ... actual good ML output on little compute. I, I should ta- I should chat with vik about this, but aside

00:12:03 Lucas Atkins: No, it’s, it is-- I think it was... Yeah, it, it, it was very much a gift going into the pre-training side because-... we were kind of already thinking, All right, how do we do the mu- you know, the most with the, the least amount of compute? But, um, you know, we-- it took us quite a while to get the cluster that we have been training large on, which is twenty-two thousand forty-eight B300s. Um, and once we figured out when we were going to get that, get access to that cluster, everything else kind of became clear as far as, like, timelines for Mini and Nano and, and when we wanted to do that. Uh, obviously, you know, five hundred and twelve H100s was easier to come across, um, for Mini and Nano. So once we figured that out, um, it really became, uh, this game of, okay, how can we find, like, the best research on the topic of, of pre-training, and what is kind of... What are the, the, the papers and publications that are coming out, um, that have enough potential and enough precedence, either because, uh, another lab used them, it comes from a reputable team, uh, the ablations and the, the evaluation setup, like in the paper, was sufficient enough to give us confidence. Uh, and then we basically spent, I don’t know, it was probably about two months just figuring out what we wanted our architecture to be for the MoE, then figuring out, okay, now that that’s what we want to do, how do we implement all of that in the actual training pipeline? Uh, how can we-- you know, at that time, there had been many people who’d done Muon, but, um, for post-training, and, and then other-- some Chinese labs had used it, but there wasn’t, like, a widely available distributed Muon, um, to do it that scale.

00:13:54 Nathan Lambert: What do you think that, like, looks like in decision-making? ‘Cause that seems like a risky decision, if you ask me. I think for one, the ti-

00:14:00 Lucas Atkins: Muon?

00:14:00 Nathan Lambert: ... the timing, the, the, like, timing sharing that you’re saying is good. Like, you said this for two months, and then, like... But, like, even Muon is like, that’s a bet that would even take-- like, somewhere like AI2, that would take some serious evidence to go with it. We would want to ablate it. So like- ... on a single track, it’s like y- you had probably had a process for becoming fairly confident in it then.

00:14:24 Lucas Atkins: It- yes, but it, it was also, like, Kimi had, had just come out, and we knew that that one used Muon, and so we knew that it, at least, if implemented correctly, could deliver a good model. There weren’t outstanding ablations done around like... You know, there wasn’t a Kimi scale model done with Adam, and then compared to Muon and see the difference. But, um, that at least gave us enough confidence that if-

00:14:50 Nathan Lambert: What does Muon give you? Does it give you, like, memory saving, uh, in-

00:14:55 Lucas Atkins: No, it’s actually a little bit more memory. It’s, it’s, it’s mostly-

00:14:58 Varun Singh: It’s, uh-

00:14:58 Lucas Atkins: ... like the loss converges a bit quicker.

00:15:00 Varun Singh: It’s, it’s less memory, actually. It’s, uh, uh, only one momentum buffer instead of Adam’s two, uh, beta buffers, and then it’s also better convergence.

00:15:10 Nathan Lambert: Okay. So it’s, like, mostly designed around convergence, and then I know the math is different, which is where this momentum term changes.

00:15:15 Lucas Atkins: Well, it, it kind of came out... I mean, it had its, its, its big, you know, uh, explosion of popularity in the kind of nanoGPT speedrunning community. So it was kind of all built around converging to a certain, you know, validation loss faster, and, uh, that, that, that was, um... As for why we chose it as opposed to Adam, we’d used Adam for 4.5b, uh, but we also knew that if we wanted to move this fast, that we were going to have to make some pretty big bets, educated. Um, but, but still, we would have to make some, some, some risky decisions, um, beyond just, you know, training in general. So, um, there were a few that Muon we went with, uh, I think was, was one of our bigger bets. Uh, we ended up not doing, like, multi-token prediction or, or, or FP8 because we were throwing so many new things into the run at once, um, that-

00:16:12 Nathan Lambert: Do these apply for-

00:16:12 Lucas Atkins: ... if something were to go wrong-

00:16:13 Nathan Lambert: um, Mini and Nano? Are those also Muon, or are those- ... Adam as well? Okay, so then you- ... you get some de-risk from that. Do you know off the top of your head how many days it take to train each of those? Like, a, a good-

00:16:25 Lucas Atkins: Uh-

00:16:25 Nathan Lambert: ... ballpark for people, before-

00:16:27 Lucas Atkins: Yeah, so-

00:16:28 Nathan Lambert: going into the bigger run.

00:16:29 Lucas Atkins: So, so Mini, uh, so Nano on it was five hundred and twelve H200s, uh, took a little over thirty days. Um, and then Mini was about forty-five days.

00:16:45 Nathan Lambert: Okay. I think another thing- ... off the top of my head is I know that, like, a OLMo 1B dense would take us, like, eleven days on a hundred and twenty-eight H100s for a dense model. So, like, sixteen. So, like, the numbers- ... just go up from there. ‘Cause then it’s like the question is like, I’m guessing i- if those are forty-five days, and then you have-- you up the number of GPUs, it’s gonna be like a similar amount of time, or forty days for the big model, but much more stressful.

00:17:16 Lucas Atkins: Yeah, the big model was... But again, that was- we knew that we, we wanted- we felt confident that we could deliver a competitive and exciting model in January 2026. Like, we knew that it would-- we could... Who knows kind of where the research and what, what class and, and, and, and skill and performance of model is gonna come out in the next three months? Um, so we also knew that we really wanted to land sometime in January, and that’s also why we also took- we went with B300s, even though definitely the largest public train of that size on B300s and, and the, um, you know, a lot of the software was not-- did not have, like, out-of-the-box B300 support. It was the only way we were gonna be able to train a model of this size in-

00:18:06 Nathan Lambert: Did you have to do this? Did you have to implement the... like, help solve version issues or other issues on B300s? ‘Cause I’ve heard that-

00:18:13 Lucas Atkins: W-

00:18:14 Nathan Lambert: ... the rollout has been rough.

00:18:16 Lucas Atkins: We had to add-... a, a bit. There, there were a couple days where the, the data center had to take it offline to implement some bug fixes. It was, it was definitely, like, a very cool experience being on the bleeding edge, but, um, also, like, a little frightening ‘cause you just know, like, “Oh, we’re not getting the most out of these that we possibly could.” So, um, a little bit of both.

00:18:40 Nathan Lambert: Uh, was your final training run stable, or did you have to do interventions through it?

00:18:46 Lucas Atkins: Uh, it was very stable, actually. Uh, it took-- the beginning of it was not. The, the, the first ten days were absolute, um... It, it would start very well and, and looked, you know, uh, the dynamics and the logs, and the graphs looked very similar to Mini and Nano, and then after, uh, around a trillion tokens, it- the- we- you know, you’d get collapsing, experts would start to go crazy. Uh, part of this is just, again, we are very sparse compared to what you, you, you have. So, um, you know, four hundred billion total, um, thirteen billion active, two hundred and fifty six experts. Like, it was, it was-

00:19:26 Nathan Lambert: Did you do a, uh, expert routing loss or some sort of balancing loss?

00:19:30 Lucas Atkins: Yeah. Yeah, yeah. Yeah.

00:19:32 Varun Singh: We did, um, we used DeepSeek’s, uh... We, we modified DeepSeek’s Auxiliary-loss-free, um, uh, loss balancing with our own, like, uh, with some tweaks, and then we also added a sequence loss like they, uh, did as well.

00:19:47 Nathan Lambert: Uh, was there Auxiliary-loss-free one from DeepSeek V3, or was that a later model?

00:19:51 Varun Singh: That was V3.

00:19:52 Lucas Atkins: It was V3.

00:19:52 Varun Singh: They did a separate paper on it as well. Yeah.

00:19:55 Nathan Lambert: Yeah. Yeah, that makes sense. I think a lot of people have derived from there. Um, have you- ... had issues on post-training as well? So I have a theory that the new algorithms we’re getting from the Chinese labs, like GSPO and SysPO, are primarily for problems that you solve when you have big MoEs and you have expert problems when trying to do the RL. And that’s the whole reason that, like, I think our very serious AI two RL setup, like, we’re doing it on dense models, and we’re just like, “It’s fine. We don’t have this big clipping problem, and as much like we don’t have as much of a need to get the batch size as big to ac- activate all the experts.” So you’re saying you have so many experts and so much sparsity, that potentially sounds like you’re making RL harder.

00:20:36 Lucas Atkins: Um, yes. I will also... I will say that from just, like, a purely post-training side, we added as much as we po- we used- we... So our code base started from TorchTitan. We’ve had to make a ton of modifications to it to get it where we need it to be, but that was an excellent base. And from one of the bigger learnings from Mini and Nano was treating, uh, at least the SFT side of it, as a s- as a separate phase. Um, ‘cause with, with Mini and Nano, we finished the pre-training, we did context extension, then we took those and then ran those on, like, the sixty-four H100s we usually would do post-training on. Um, that presented a lot of challenges, uh, with the MoEs. They, they really... And that’s kind of been a thing in the open space, is post-training MoEs, like, really, um, can be frustrating, even for SFT. So for Large, we added, uh, like, fine-tuning directly to TorchTitan, um, and did it all on the same cluster. So, um, from a performance standpoint, like, SFT was very, um... actually ended up being totally different.

00:21:42 Nathan Lambert: What is the actual difference between the q- the, the implementations then? Is it just kinda like you end up with different batch sizes and parallelism and stuff? Like why-

00:21:50 Lucas Atkins: Uh, I mean, we ended up, we... Yeah, we ended up needing to get it to do really, like, to get context parallelism really well, really good, ‘cause we’re obviously going at a higher sequence length, and then, um, just adding the proper loss masking. Um, it, it, it, it ended up being a relatively easy implementation, especially ‘cause we did all the pre-processing, uh, outside of TorchTitan.

00:22:13 Nathan Lambert: Interesting.

00:22:14 Lucas Atkins: Uh, and then on the RL side, yes, I would say it’s not, um, it didn’t present itself as, as, as significantly harder than, than, um, Mini and Nano. However, that many GPUs does, so we didn’t end up using, uh, two thousand of the B300s for that. That ended up being, uh, a thousand. So two, we just split the nodes in half.

00:22:39 Nathan Lambert: Yeah. That makes sense.

00:22:40 Varun Singh: On the dense model side of things, uh, you mentioned that you didn’t need to use all the tricks and stuff. I, I think it is, uh... I think the, the, it- MoEs are just, in general, harder to RL, but I think it’s also, like, uh, b- because of, like, the KL mismatch between trainer and inference engine, right? Um, where you have, like, uh, sometimes the inference engine can pick different experts compared to, like, the trainer, uh, when you, like, do a forward pass on the same tokens. So I think there is definitely some, like, inherent instability with, with RL on MoEs.

00:23:13 Nathan Lambert: Yeah, that makes sense. Are, are... Okay, um, another question of, like, how much do you want to say? How do you feel about the state of public post-training recipes? Like, do you... Like, I, I feel like there’s so little out there, and there’s an opportunity to be seen as technical leaders by sharing just, like, more of what you’re doing. ‘Cause I feel like we’ve seen for years how complicated things can be, but also at, kind of at the same time... Like, we see this from the likes of Llama, has these really complicated recipes. But at the same time, I feel like just executing on a simpler recipe can get pretty close. But it’s just, like, very uns- I feel, uh, currently unsatisfied with how much I know about what are the actual core trade-offs of doing post-training well. And I think you could do a lot with SFT, but there’s definitely, in this RL regime, more trepidation of kind of narrowing your model to either downstream use or, like, being able to do this multi-week RL run where you get the most performance.

00:24:06 Lucas Atkins: Yeah, I mean, I, I, from-- since RL has become such a pivotal part of the process beyond what, you know, DPO and, and, uh, and kind of your, your typical RLHF was in the past, like, we used to get quite, uh-... sophisticated with, with how we would do SFT and, and even our, our RL. We, we obviously, we make MergeKit, so we, we utilized merging, and we used to do a lot of distillation, um, to eke out as much performance as we could. Now that RL is such a massive part of the entire post-training stack, I, I have almost reverted us to just really solid but simple SFT. Um, like in, in large, I mean, we’ve-- our post-training data set for, uh, Trinity Large is, uh, two hundred and thirty billion tokens. Like, like, it just like a really, really, really large-

00:25:09 Nathan Lambert: That’s ten X what we did. At least in SFT.

00:25:10 Lucas Atkins: And even that-- and even, even your tenant, like that was bef- before this kind of w- going at this scale and even kinda thinking and, and reasoning models. Like our largest SFT before that was five billion to-- we’d do, like, three epochs, but it was like five billion, you know, tokens, so- Um-

00:25:28 Nathan Lambert: Our non-reasoning model is, like, te- another ten X. So, like, our most latest instruct model is, like, two billion.

00:25:34 Lucas Atkins: Yeah, which is, uh, already a lot, you know. So, um, I, I’ve definitely... We-- you know, simplicity’s key because it also makes debugging anything easier, and then, um, devoting a lot of that sophistication to the RL. Our RL part is, like, really important. I do think that, I mean, the next, uh, phase of reinforcement learning for models of this scale is, is just scale. Is, is... Okay, we went from, you know, twenty billion SFT to two hundred and thirty, now we’re going from, you know, ten environments to a hundred. I think that that really is where you’re gonna get the biggest benefit. I also think that’s why, you know, MiniMax and, and, and other players like GLM are so performant and just, like, have that extra bit of, of usefulness that goes beyond just what you see in the benchmarks, is they’ve, they’ve really embraced, like, long-form, uh, RL. And, and so, um, yeah, I mean, to be quite frank, our, our RL pipeline’s rather... immature might be the wrong word. Like, it’s, it’s, uh, there’s definitely a lot more work we could do and a lot more work we need to do, but, um-

00:26:43 Nathan Lambert: Have you started the tool use side of RL?

00:26:46 Lucas Atkins: That-

00:26:46 Nathan Lambert: Or are you mostly... Well, um, beyond like, if you’re training on code, just verifying the code answer, I don’t count yet as tool use. I would say, like, search and code integrated reasoning is what I think is gonna be like minimum table stakes, but do it- to do it well is really hard. Like, we have to, like- ... like, you, you really, like, uh... That’s what I want to do. I want all of our models to have that this year. Search is prob- you have to have, like, a partner to do search or just, like, illegally scrape Google if you’re gonna- ... you’re gonna serve this model onto a customer, and it’s gonna- ... what? Go, go to Google, like, what?

00:27:16 Lucas Atkins: Yeah. Yeah, no, I mean, I, I... Beyond, like, like, really kind of like long-form, like deep research or, um, you know, even like GPT-OSS style or, or G- GPT 5 style, where, you know, it’s doing a hundred tool calls before it gives you a response. Not there yet, um, but that is kind of... Once we get past the, the final kind of RL of Trinity Large, and, and we kinda look at where we go next, like, that is the next major hurdle, um, for sure, and it’s intimidating.

00:27:56 Nathan Lambert: How big is your, your team of- of... Like, how many people are spending the majority of their time on the model? And then I think we c- start to wrap up technical talk and zoom out a bit to ecosystem and company strategy.

00:28:09 Lucas Atkins: Uh, there’s thirteen at Arcee- ... that are just, like, every, every single day is working on it. Yeah.

00:28:16 Nathan Lambert: And I guess that’s a good number because these people are talking about data, but there’s also, like, the whole data thing that’s coming somewhere else. But also somebody else that wanted to pre-train a model, like they could just download the best fully open data set. And I don’t think it’s gonna be quite as good, particularly in the fact that, um, like, if you look at OLMo’s models, we don’t have a lot of tokens, so we need to, like, acquire- ... more tokens in the open still. But to, like, get a number of thirteen, where some are spending a bit of time on data, but there’s the whole data abstraction, is actually kind of nice for somebody that’s like... To do a serious modeling effort, you need to have this many people, I think.

00:28:50 Lucas Atkins: It, it was-

00:28:51 Nathan Lambert: It’s reasonable to me.

00:28:52 Lucas Atkins: It was, it was a good number. I mean, I would say that, um, it, it was helpful to be able to, you know... This was like, how do we alleviate as many concerns as possible? Or how do we check off as many boxes, right? And it’s like, if we’re trying to do this in the shortest possible amount of time, like, we need to focus on what we’re good at, which is we- pretty good at post-training, and how do we get to the point where we’re able to do that? Well, we have to have a pretty strong base model. How do we get a strong base model? We’ll-- we have to, you know, figure out how to do it, perform, you know, efficiently across many, many GPUs, and then data’s, you know, extremely important, so getting a partner that could, you know, help us with that, and we could offload some of that. It, it- there ended up being, obviously, as you, you know, alluded to earlier, like, a lot of, uh, working with Datology and, and, and others to make sure that the data accomplished what we needed it to. Um, I think that that is gonna be an interesting... You know, as we, as we- now that we have Large and we’re looking at, you know, kind of going further, it’s like, okay, you know, the, the pre-training data really has to be in service of what you wanna do in the post-training, uh, work.

00:30:10 Nathan Lambert: How did you identify this?

00:30:11 Lucas Atkins: Like, like-

00:30:11 Nathan Lambert: Like, like- ... did, did you identify this through Mini and Nano, or, like, how’d you come to think that this was so important?

00:30:19 Lucas Atkins: Data in general or, or just-

00:30:20 Nathan Lambert: Or like this in form of post-training

00:30:21 Lucas Atkins: ... of optimizing it for the post-training? Um, I- really ob- observing other, other players, I think. I mean, it’s, it’s... You know, the, the true base model has kinda stopped really being a thing.... around Qwen2, but definitely around Qwen 2.5, um, where you started to see how much post-training data was making its way into the, the, the base models themselves. Um, and then you start to see the models that have done that, how malleable they are with RL, Qwen 2.5, Qwen3 being a good example. And you start to see like, oh, yeah, like they are, uh, doing as much in the last probably thirty percent of training to make it so that when they go to do RL or post-training, they’re gonna have a really good time. Um, you know, they’re just complete-- they’re way easier, way more malleable, way more performant than what you had in Llama 2 or Mistral 7B. So, um, I knew that i-in-intuitively, kind of going into this, but it wasn’t until after Mini and Nano, yeah, where, where we kind of... Well, definitely 4.5B, where we were like, “Yeah, we definitely need to juice our mid-training quite a bit.”

00:31:31 Nathan Lambert: Yeah, I agree. Okay, this was fun. We could- we’ll probably revisit themes from this. I think that, um, I can definitely go over time and keep chatting because I’m enjoying this. And for context, Mark and I had coffee at some point when I was at some conference in SF, and I was like: Damn straight, this is a fun bet that you’re making. So I’m trying to recapture as much of this as you can. Um, for context, it’s like in July, which is similar to when you decided to start this model, which is when, like, Qwen Coder came out, Kimi came out, um- ... GLM 4.5 came out, and I was just, like, looking- and Llama had kind of been, like, become a meme of going away. And that’s why I launched the Adam Project, where I was like: Come on, we need to have some people doing this. And I think that it’s, like, hard in the US because I think there’s so much money to be made on AI. Like, the company- the big tech companies are like: “We see it, and we’re gonna take it, so I don’t need to bother with, like, caring about open models ‘cause we don’t need it.” But from, like, an ecosystem co- perspective and a long-term tech perspective, I don’t think that works very well for the country. So it’s kind of this weird middle ground of like, how do you convince people to actually build open models? I was on... Like, I have calls with people in government asking me, like, what would I actually do? So it’s, like, very hard to think about this. And I have this- and then it’s just, like, to hear that you guys are just making this bet on this is very fun to me, but it’s also, like, based on actual learning from trying to do this. So you’ve been trying to train open models. I think Mark and I have both been at Hugging Face in our past, and you’re, you were trying to sell people on using open models, and there is a market for this, but it wasn’t enough to not have the base models. So I think, like, talking about your experience in selling on-prem open models and why you needed to train your own end-to-end, and why you needed to train bigger, is great because I hope there are more stories like this, and it kind of fills a void and inspires people to work in it. So how- however you want to take this prompt.

00:33:24 Mark McQuade: Yeah, I can jump in. Um, I mean, yeah, I mean, wh- when I started Arcee in 2023, right, uh, it was... All we did was post-training. Uh, and we worked with, uh, a lot of large organizations and did model customization, you know, for their use case on their data. Um, and we were using Llama-based models, Mistral-based models, and then, you know, some Qwen. I don’t even know if we actually did much Qwen, right, Lukas, at that time, but-

00:33:54 Lucas Atkins: No, we did. Yeah, we, we- Later on, but and then-

00:33:56 Mark McQuade: Later on, right? Uh-

00:33:57 Lucas Atkins: We did, and then we ended up not, because after a lot of Chinese models started to come out, then the companies didn’t wanna use Chinese models, so then we kind of went... Yeah, it was kind of just tricky.

00:34:08 Mark McQuade: Yeah, and people don’t realize that that’s real.

00:34:10 Nathan Lambert: People don’t realize that that actually happened.

00:34:13 Mark McQuade: Yeah, no, that’s, that’s a real thing. That’s why we, we started going down to pre-training was because, well, you know, Meta did their thing and kind of got out of it, right? So there was the, the main US player got out of it, and, and we were working with a lot of US-based enterprises that were not comfortable using Chinese-based architectures. And if you wanted to use the best open models of the day, it started to really trend towards, you know, the Chinese labs. Um, and to the point where we are now, where it’s like, you know, ninety-plus percent of the top mo- open models are coming out of China, um-

00:34:47 Nathan Lambert: Yeah, like, Cursor’s building on it and stuff. Like, people are building on these things.

00:34:52 Mark McQuade: Yeah. So, um, we said, “Okay, let’s...” Instead of we were so reliant on the Metas of the world, the Mistrals of the world, and Mistral largely stopped open sourcing, uh, you know, fully. So we said: You know what? We’ll just go down the stack, and we feel we’re capable enough to, to, to train our own models from scratch, and then we control the, you know, the stack. We can, you know, we, we control the core of, of... as opposed to relying on others to release great models. And, um, and then during this time, you know, it just happened to be that, um, you know, there wasn’t a tremendous amount of US companies doing it. So, um, from our perspective, it was kind of a, a win-win, in that we were able to own more of the stack by going down to pre-training and creating our own models, as well as we were entering into a, like, a space that there wasn’t a tremendous amount of competition, to be honest. Um, and, you know, I-- Lukas and I had said this yesterday, I, you know, I think as a startup, every startup do