That's pretty wild that a Ctrl-F of "qwen" or "china" or "alibaba" on the thomson reuters announcement URL turns up no results. They want to portray it like they developed this thing from scratch, when that is by no means what actually happened.

They didn’t say they created it no? I don’t think any of those points matter. It’s a business announcing a new product. Why does it matter if they built it internally or used external teams? I find strange to focus on those details

40M$ to get a marginally better model is surprising, why not just use the free weight models

Full technical report PDF: https://huggingface.co/spaces/tri-fair-lab/publications/blob...

> In this report, we argue that frontier performance can be achieved by a wide range of institutions through Continual Learning on readily available open-weight models.

> As opposed to existing limited approaches such as small-scale fine-tuning, prompt engineering, or tool-augmentation with a frozen model, our Continual Learning approach takes advantage of the effectiveness of a modern mid- & post-training stack while introducing safeguards preserving both plasticity and stability at each training stage and seeking to make the minimal number of high-impact interventions on the parameters.

For the large model, Thomson is utilizing the fine tuning stack they describe in the article, running it on Snowdon 1.0-Large, which in turn is a fine tune of Qwen3.5 397B. Same thing for the small model, but it's a fine tune of Snowdon 1.1-Small, which is a fine tune of Qwen3.6 35B.

As for the small version's run:

> The full pipeline consumed approximately 1.63 × 10²³ FLOP over 35,207 B200 GPU-hours, showing that these results are achievable with compute and personnel budgets substantially lower than commonly thought.

That would amount to around a quarter to half a million dollars of spend on that run. 100k minimum, if they got a great deal.

Thanks, that’s great, lots of details

So it's a qwen fine-tune?

I mean that's a reasonable thing to do, but then the press release shouldn't be written the way it is written.

They're not as detached from the rest as the industry as the writing suggests.

__

> It is obtained by repurposing the open-weight Qwen3.6-35B-A3B model and substantially improving it on a wide range of performance domains.

nice wording on the HF page tho. "Repurposing". Lmao

From the HF link posted above it's Qwen3.6-35B-A3B.

https://huggingface.co/thomsonreuters/Thomson-1.0-Small

Per Business Insider [1], it was built on Qwen.

[1] https://www.businessinsider.com/thomson-reuters-builds-ai-mo...

> Running something internal with 30% GPU utilization is just too cost inefficient compared to using an API. Idk how Reuters will manage to solve this fundamental problem.

That's actually easy, because you can solve it through doing nothing and simply declaring that optimizing for lowest cost is not the main goal.

The fundamental-ness of that problem is entirely man-made and thus can easily be declared void as long as you have the cash to back that up.

Which might be a winning strategy in a world where everyone else is not doing that. Plus that your knowledge stays in-house, etc.

What makes sense also depends on one's business model: TRI charges premium dollars for access to their systems, so there is no need to optimize for cost; trust in the answers is the currency of knowledge workers in today's complex domains.

At the Thomson Reuters family of companies (technically then: Refinitiv Ltd. sold to LSEG), the first foundational model (in the sense of "trained entirely from scratch") was trained already in 2018 (i.e., pre-ChatGPT); it would even have been earlier, but the electricity wires and fuses in the rented 5 Canada Sq, Canary Wharf office had to be replaced first at the time to deal with the current needed to serve the GPUs.

It’s based on qwen, not fully trained internally. I expect we will way more of this in the future, it’s pretty cheap to fine tune an open weight model for your specialized niche

Yep, this is the fundamental issue. It's a 35BA3B model and they probably finetuned it and evalled it in one bursty week on an 8xH100 rental just fine. But long term inference is always going to be easier in an API.

Unfortunately for reuters tho, they dont really have a choice. A lot of their data moat is not necessary live data as in linkedin, and the only way they can keep that moat is by doing this. I guess that justifies any cost.

It’s a cost-saving measure and a marketing story.

This is it: https://us.aws.cdn.hf.co/xet-bridge-us/6a899b56f043f237307da...

It’s on hugging face, I suppose one could do those themselves?

https://huggingface.co/thomsonreuters/Thomson-1.0-Small

Reuters, the news agency, is only roughly 10% of TR's revenue. They're still a pretty big player in Legal and Tax.

I doubt the point is to "make the money back".

It's likely split between two goals:

1. Marketing and expressing to their customers that they are not falling behind, and

2. Insulating themselves from frontier labs jacking up prices, nerfing the models they depend on, or otherwise unexpected changes in behavior.

I think the main goal is #2. Thomson Reuters might be a $40B company, but.... at this point it's not clear that that holds any weight in terms of not being fucked over by 2 companies aiming for $2t+ IPO valuations.

Edit: On second thought, there is probably a #3 too. They can serve inference for their own models significantly cheaper than frontier lab rates (assuming they're capturing continuous use of their hardware). I still think #2 is the primary goal.

I doubt that 40 million are a lot of money for TR.

I also doubt that R&D spending having to "make back" directly is a winning strategy.

No it's just a value add to their existing data products and a moat against the big n LLM companies. The "news" part of the business is relatively small compared to everything else the do.

Like how Bloomberg does news but its far from their only or primary product. TR covers a different surface of data products than Bloomberg but it's a decent comparison.

I know you're being facetious, but I genuinely think Thomson Reuters should be investing in NFTs as much as it is in AI. NFTs (while some of the shine has admittedly worn off) are an emerging infrastructure for digitally native ownership, and that's precisely the sort of institutional problem Thomson Reuters is positioned to solve (think tax records, medical records, etc).

People in tech suddenly tossing NFTs to the side because AI came along makes no sense to me. HN should be as bullish on NFTs now as it was in 2021. Jumping on to the AI train and acting like NFTs are bad now makes us seem flippant.