Thomson Reuters Launches Its Own Frontier Model
thomsonreuters.com
thomsonreuters.com
How I understand it, without really reading into it:
- Thomson Reuters did not "create" a frontier model, they gave some money, maybe a bit of their data to Imperial College London, and took Alibaba's Qwen 3.6 35B A3B Model for a basic fine tune.
- "They" (some undergrads at Imperial College London) used an existing ablation framework to undo some of the topic alignment of the original model.
- "They" fine tuned on some domain knowledge trying to preserve general knowledge - here maybe, just maybe some data came from Thomson Reuters.
As a result: one of 100's of Qwen 3.6 35B A3B sparse model fine tunes, just for publicity, to write overstating headlines like "X created their own frontier model"
It's going to become another way to monetize your informational assets if you're a big older enterprise with troves of data. All you need is time to figure out how to make it useful for yourself and then eventually sell access to it however you want.
Think of all the data that big orgs have that isn't accessible to all the AI labs to suck up.
Reuters News is only slightly over 11% of their business. Legal and Accounting is closer to half.
on edit: just went and looked it up, adding in compliance offerings it is over 80% of their business.
If you are a highly specialized professional intellectual property paralegal, a forensic tax investigator or a post-market pharmacovigilance analyst, you will need for-profit knowledge sources, and your answers will often require synthesizing and interpreting multiple sources.
China is fucking smart. They have technical expertise to know that by feeding the model certain data, they can shape the outcome of questions posed.
Yeah, in the end, maybe both US and Chinese models can solve Fizz Buzz, or some Erdős problem.
And they can answer our inquisitive minds when we wonder, "Why is the sky blue?".
But deep questions about what is normal in the world they can change the outcome of.
And "Why is the sky blue?" is a deeper question than we think. Because it isn't blue everywhere in the world right now. And whether it is the fault of the US automotive industry, or aggressive investment by China in their own manufacturing base matters.
Of course, both have been responsible for pollution at different times. Growing up in Michigan near Dow Chemical I know this.
But depending on how they select training data answers can be nudged one way or another.
The US is smart too, and they have people working on the same things. It's ironically easier for me to talk about how China's security services are likely to shape their models' perception of the world. Which is a damn shame, because we deserve unbiased answers so we can all help our country improve.
Or countries improve if you want to take a global shared world view.
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
(Full disclosure I’m a TR employee, although I had nothing to do with making this)
> 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.
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
Which is the thing with press releases. Would've been nice to not do the bare legal minimum tho
Sounds like they spent $40 million finetuning an open weight model on their own data? I wonder what they built on.
[1] https://www.businessinsider.com/thomson-reuters-builds-ai-mo...
But this is qwen based.
But w/e I'm pro AI so more companies having more people with skills for more post training is cool
It eventually stopped making sense because of inference costs. 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.
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 won't match Anthropic or OpenAI, but it could be economic?
There is no such thing as "secure infra" hosted by someone else.
This might still be fine, depending on your threat model, of course, but if your weights absolutely must never leave the confines of your org, you cannot use any shared hosting provider, because they just offer legal coverage of incidents. But if your moat is your knowledge, legal doesn't matter as much as the knowledge being suddenly unmoated.
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.
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.
That’s it? It was generally competitive with leading frontier models? Neat, but why would someone pay for frontier models and also a generally competitive additional product?
Looking forward to the ERP fine-tune.
This feels very much like a news agency getting into crypto or launching its own NFT line.
Or IBM selling Watson.
Or Mozilla chasing every which thing.
They're not stakeholders in the future of work. They're just wanting to stay relevant and pattern matching against what they see.
Reuters is too important for this.
If they were trying to use this as a narrative affront to OpenAI and Anthropic, maybe, but this is Reuters, not a deeply political organization seeking to land gotchas against big tech.
https://ir.thomsonreuters.com/news-releases/news-release-det...
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.
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.
It's exactly the same sorta thinking re; Microsoft potentially fucking over the PC videogames industry that Valve used to justify the zillions of dollars and countless man-hours put into their big push for Linux gaming rather than tie themselves to a single proprietary company that could try to kick them out of the gaming industry. So far it's going pretty well for them. Depending on how they play their cards, this could also work out really well for Thomson Reuters as well.
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.
I also doubt that R&D spending having to "make back" directly is a winning strategy.