Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI

Good morning, everybody. My name is Ari Morcos, I'm the CEO and co-founder of DatologyAI, and really excited to kick off the data quality track today. Data quality is what we live and breathe at Datology; it's all we think about.

In fact, the company's name literally means "the science or study of data," so very excited to see the increasing excitement and interest in this area, and an amazing lineup of talks today. So today I'm going to tell you about why data quality is a compute multiplier that we're all overlooking, and where we can make massive gains by just working on better data.

We've seen that compute availability over the last 6 months has become extremely scarce and is only getting worse. We saw H100 prices reverse—there's several year-long drops, which is normal for hardware—and all of a sudden come up where now they're about 40% up from their lows at the end of last year.

As test-time compute has become a critical part of models, and as we have put more and more thinking tokens in, we're seeing that the number of tokens usage is absolutely skyrocketing. Reasoning models use 8 times as many tokens as non-reasoning models, and that's projected to 5x again in the next year or so.

So the number of tokens we're pushing through goes higher and higher, and that constrains compute even further. And this has led, actually, to a world where it's not implausible today that we might see access to some of the frontier APIs actually get limited or go away.

As one example, Google just capped Meta's Gemini usage because of inference constraints. OpenAI has effectively started selling token futures, where you can guarantee token capacity some amount of time into the future. This is only necessary because they are legitimately wondering there might be a world where access to frontier API tokens is limited, not as a business decision, but because there's simply not enough inference, and first-party products will be prioritized.

So in a world where compute is increasingly scarce and you need to make models better, well, what do you do? Well, we work on data. You're going to hear me say this over and over again: data quality is a compute multiplier, because what it does is it makes the learning curve steeper.

So this is just a very simple schematic of performance on the y-axis as a function of data on the x-axis. Note that this x-axis you could swap it out for data, for compute, for time, for dollars; they're all the same x-axis fundamentally.

And if you can make data quality better, you can turn this gray curve into this blue curve. And that means that now you can get dramatically better performance for the same compute budget as if you had trained with far more compute.

And similarly, you can get the same performance for a much smaller compute budget, which is exactly showing you how you can get performance as if you had spent 10 times as much on compute, and really shows this compute multiplier point.

So how do you actually do this? Well, fundamentally, the idea is we want to make it so that we get the maximum signal per token and per batch. For those that are a little bit more technically, what we want to do here is maximize the marginal information gain per data point when we show it to the model.

What data is going to teach the model the most? And that's all about finding data that's relevant to the use cases that you want. One thing that is very important is that there's no one golden dataset to rule them all that's good for everything, no matter what you want to do.

A dataset is only going to be optimal with respect to a particular set of output tasks that you want the model to do. So if you want a great legal model, you're going to want legal data more than healthcare data, and vice versa.

It needs to be diverse. A lot of the issues we see with model robustness and brittleness come from training on data that's not diverse enough. So the model can answer a question correctly if it's presented just so, but if it's presented a little bit differently, now everything breaks.

It needs to be information-rich, and you have to mix the data correctly. This is a hugely difficult part: you now have many different sources. How do you combine them to actually drive the largest improvement in performance? So this is a high level of what we do at Datology here.

You can think of us as the oil refinery for data. We don't source new tokens like many data providers; rather, we take existing tokens coming from public datasets, proprietary datasets, and licensed datasets, and make them way better. And how do we do that?

Well, we do that through these four C's: clean, curate, create, and compose. So cleaning is fairly straightforward. This is doing things like heuristic filters, all-ago for, and things like that, removing documents that have only 10 characters in them or are all winged in.

That's kind of basic table stakes. Benchmark decontamination is incredibly important. As I'm sure you all know, benchmarking has become a real problem and makes it very difficult to interpret model results. So we rigorously decontaminate all of our training data with respect to all downstream benchmarks, with a pretty low n-gram to ensure that that's the case.

That gets you to a point where now you can feed the model into the data, but it's still—sorry, feed the data into the model—but it's still not very good. So then how do you make it better? Well, it's a combination of many things, ranging from quality classifiers and taxonomy across different topics, and balancing that.

Redundancy reduction, so removing data points that are not the same, that are semantically similar but convey very similar information, even if they're not the same pixels themselves, say. Upsampling and downsampling data points based off the quality and the relevance.

And then task distribution matching, identifying what data do you actually need in order to solve this given task. That now gives you a dataset that is very high quality but is typically still too small. And that's where synthetic data comes in.

Now we can go and rephrase that data as effectively a very fancy form of data augmentation to produce dramatically more data in many different formats. And this helps a lot both with kind of data size and with diversity, because we can really inject a lot of diversity into this.

And then finally, you have these datasets. How do you combine them, and how do you sequence them across different training stages? It's now become table stakes that any large model is generally trained for at least three phases of data.

How do you do that? And can you actually even do continuous curricula and things like that, which is a lot of what we work on at Datology? And that ultimately gets you a much better dataset out. Allright, so that's a high level of kind of what we need to do.

What can you actually get out of this? Can this actually really make a massive difference? So about half of our team at Datology are just researchers, and we do all of our own research on how we do data curation effectively.

Because this is such a critical part of the model-building pipeline, there's very little published here, because there's a very strong disincentive not to share how you do this. The kind of foundational paper for Datology was one I wrote when I was at Meta called "Beyond Neural Scaling Laws," which was fortunate to get a best paper at NURIPS a couple of years ago, which showed that if you choose your data correctly, you can actually bend the scaling laws itself.

You can change the exponent. And that's because you're now not wasting your time looking at redundant or unnecessary data. That was very much the proof of principle for all of Datology, and we've since expanded this into many public research releases we've shared of various ways to improve models just through data curation.

I'm going to go through a couple of those results now and show you what we've been able to achieve. So first, let's talk about vision language models. How can we improve VLMs just through data curation alone? So in this case, what we did is we took the Mammoth dataset—this is a fairly small dataset, about 25 billion tokens—that we use for the purposes of training the fusion adapter layer between your

text model and your vision model. And what you can see, this is a scaling plot where we have error on the y-axis as a function of log flops on the x-axis. So there's about a scale of 1,000 from the leftmost part of this plot to therightmost part.

And what you can see is that if you look at the Pareto frontier defined by many of the best public VLMs, like the Qwen 3 series and 3.5, Intra and VL, et cetera, you can see that a model trained on Datology's data is able to go well beyond that frontier.

And I'll note, this is actually without any post-training as well, so you can get very strong performance across many different benchmarks. So just looking at kind of taking the input dataset, that gray diamond there, that's the input dataset we use to do our curation.

You can see that just through curation, you're able to get around a 14 absolute percentage point improvement, holding everything else constant just through better data alone. And not only that, you can also see that we can roughly match the performance of Qwen 3.54b, come with about a percentage of it, while using 145x less training compute.

In a world with less compute, how do you do more? You make data better, and now it's as if you had 100 times the compute. Interestingly, I mentioned that reasoning models are using tokens at a very high rate as well.

Well, another thing that we've found is that data curation can also lead to more concise answers, depending on how you represent the data. So what's plotted here is the mean number of tokens per response across all the same set of models for the largemost part that we just showed.

And you can see that models trained on Datology, those three blue linesright at the top, are all extremely concise. And if we do kind of the same sort of plot, but now on the x-axis, instead of log training flops, this is now log flops per response.

So this is inference efficiency. You can still see that we go well on by beating that Pareto frontier and roughly get similar performance to Qwen 3.5, with 35 times fewer flops per correct answer. So data curation can make a huge impact in VLMs.

What about text models? One of the most challenging things about many models is that they work very well on English data, but they don't work well for non-Western use cases in general. The internet is an extremely biased view of the world that does not represent the world uniformly at all, and this has major implications for fairness and for the usability of these models across the world.

I don't want to live in a future where only developed countries can access this very effectively. So how do you do this? Well, curation, again, can be a massive lever here. So what I'm plotting here now is a similar plot: error on the y-axis as a function of log flops, about 100x going from left toright here.

This is highlighting multilingual MMLU performance. You can see we have a Pareto frontier here defined by many models, the Qwen models, some of the liquid models. The green square is Tiny AYA, cohere's best multilingual model. You can see, again, that we're well off the Pareto frontier, with a couple of things I really want to highlight.

First off, we only use 8% of the data here as multilingual tokens. So most languages actually only had, at max, 6 billion tokens here. So these are not massive amounts of data in the non-English languages that are going in here.

You can again see we get the same sort of compute multiplier effect. We're a little better than Qwen 3, while having roughly 8x less compute budget here. So you can make a huge improvement. One last thing I want to show here is that if you look at the two blue points on the upper left here, those are both dense Llama-style models trained for a trillion tokens on curated data.

The point on the lowerright here I'll come back to, but is a model trained by one of our customers, Arcee AI, Trinity Large, that was trained on 17 trillion tokens and is a hyper-sparse MOE. And what you can see is that if you take the line defined by the two smaller models, it goes mostlyright through that blue star, which is Trinity Large, despite it being trained with 50x more training compute.

So if you use your data correctly and you simulate token scarcity appropriately, you can also get very predictable scaling to much larger models, and you can de-risk a run with 50 or 100 times less compute, effectively, before you actually go and scale up the hero run and find that maybe it doesn't end up where you want it to be.

Another interesting scientific result I wanted to share here is that we also see very strong cross-lingual benefits from curation. So what's plotted here is the non-English accuracy, where the left bar is showing not curating anything at all, and then theright bar just curating the English.

We also curate all the non-English data, and that leads to much better performance. But I just wanted to show this, because I think it's quite interesting, that curating English data benefits non-English performance. And that's because we see this cross-lingual transfer, where the model understands how English relates to, say, Spanish, so therefore making it better at English would also make it better at Spanish to some extent.

And interestingly, we see the magnitude of that transfer is strongly correlated with the similarity between English and that language. And we also see it go the other way, although the effect's a little bit smaller, where curating the non-English data also helps to benefit English data performance.

Okay, and let me talk a little bit about synthetic data. We take an approach to synthetic data that we call rephrasing. This is something that our team pioneered several years ago and has now become basically table stakes for building a very strong model.

And anyway, I think we'll hear a lot about synthetic data in various forms throughout the day. But fundamentally, with Beyond Web, our goal is, how can we define a synthetic data platform that works extremely well and can be applied to anyone's proprietary data and documents?

Fundamentally, we want to help folks build models that wouldn't be able to do so otherwise, and that's where data quality can make an absolute difference. So to give an example, we might take a document like this about a corporate takeover, and we might convert that into one of hundreds of templates, one of which might be a series of true/false questions.

By doing this, there's a couple of things that are really great. Number one, because all the information's coming from the document on the left, you don't have any issue with model collapse. And you can actually train models that are much better than the rephrasing model, because the rephrasing model doesn't actually have to teach and understand all the concepts.

All it needs to do is transform the left document into a true/false question accurately, which is a much easier task. And then you do this into many, many different formats throughout data. This effectively increases diversity, and it makes us so you learn a lot more from the highest quality data points.

One thing that's really critical here: what do you rephrase? All documents are not created equal for rephrasing. If you just pick random sets of documents to rephrase, you will not get a great result. But if you find the high-quality documents and rephrase them, it can make a big difference.

And we've seen, if you compare this to lots of other public synthetic corpora, we can get much better performance much faster. And critically, this can be applied to any proprietary data in one of our customers' own environments. Allright, let me spend the last few minutes just quickly talking about a couple of the things we've seen with our customers where this can actually drive really gains.

So first, one of our customers, Thomson Reuters, has really focused on post-training quite a bit. And they have a very sophisticated post-training infrastructure with the goal of building better legal models on their proprietary high-quality legal data that they have.

So we partnered with them to mid-train a model first on a combination of their data and public data to then make much better legal reasoning models. So what do we see? Well, first off, look at the left here.

In this case, we took an open-source model and then just did continued pre-training or mid-training on 100 billion tokens. What you'll see here is that we see that legal capabilities go up about 5 percentage points, as measured by LegalBench, after you do this 100 billion mid-training, which was less than 1% of the pre-training budget.

But you don't get catastrophic forgetting. You also see the general capabilities go up as well. I'm sure that many of you have seen or experienced, when you try to adapt the model to a particular domain, you lose general performance, not if you use the data correctly.

The key here is actually the majority of the data we showed the model was actually data that was representative of the pre-training distribution. There was only some of the domain-specific data, and that's necessary to prevent the model from losing the capabilities that it had before.

And you can solve this entirely through better data. But this actually isn't the most exciting part here. As I mentioned, the TR team had done a lot of work on post-training and had a very sophisticated post-training harness. Well, they then applied that to the mid-trained model versus just the default instruction-tuned model.

And what they found was that the gain deriving from post-training—so the y-axis here is a delta as a result of post-training—almost tripled when you applied it to the mid-trained model versus to the just default instruction-tuned model. And that's because its policy, when it starts, is now much more accurate, and it can make much better inference.

So even if you don't change the post-training data at all, showing your model better domain-specific data can actually make post-training two to three times more effective out of the box, which I think really goes to show not only how important data can be in these factors, but it also actually goes to show how we really should be thinking about all these stages synergistically, rather than as three completely independent stages of pre-training, and then I hand it off to somebody else who mid-trains, and then I hand off to somebody else who post-trains.

Okay, in the last minute or so, I just want to quickly talk about one other one, which is Arcee AI, who I mentioned, that large model, Trinity Large. You'll actually hear from Varun, who is the pre-training lead for this model later today, so look forward to that talk.

But in this case, they trained a model—this is a fully open US-made model—on 17 trillion tokens that we curated from public datasets, no proprietary data involved here, and no closed model usage, so no asking Claude to do this for you.

And with that, Arcee was able to train a model that is competitive with the open frontier, matches GLM 5 and Kimi on many tasks, and even outperforms Claude on a couple of tasks. But I think what's most exciting about this is that the Arcee team had not trained a model prior to the middle of last year when they started working with us.

And critically, in total, across salaries, across compute, across R&D, across everything for this and several other models, they were able to get to a model that's competitive with the open frontier for less than $20 million total. That includes all the repetitions, that includes compute, that includes everything.

So if you hear this story over and over again, "Oh, if I want to customize a model, it's going to cost hundreds of millions of dollars," that's just not true. You can train an immensely powerful model, especially in a narrow domain, for high six-figures million dollars, it's very doable to get a model that's extremely performant.

This is for a general purpose, so this is kind of the upper bound of that. And data quality is how you can do that. Allright, so last slide here, just kind of summarizing. Focus on kind of what's going to give you the most signal per token.

That's the thing that matters a lot more than more tokens. It is almost always better to repeat high-quality data than it is to show low-quality data at a certain point, up to a threshold. But focus on kind of how can you get that.

Data quality remains the single most under-leveraged compute multiplier. If you're sitting in a world where you want to build a model or customize a model and you're limited on compute, how do you get past that? Invest in data.

And that's something that can do a tremendous amount of effort. And then finally, this is a frontier research engineering problem. You need to be able to score and understand data across many different axes. That's a frontier research problem, and then have that scale up to petabytes of data massive scale.

And can be very critical, and it can lead to tremendous leverage. And with that, I'll say thank you. I will note that we're hiring for a bunch of different roles on the left here. If you're interested in building or customizing your own model and would like to get much better data out of the box or apply it to your own data, I would love to chat with you.

And thank you very much.