DE version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
75% Positive
Analyzed from 1035 words in the discussion.
Trending Topics
#thomson#data#reuters#models#small#fine#model#big#https#frontier
Discussion (23 Comments)Read Original on HackerNews
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.
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
Sounds like they spent $40 million finetuning an open weight model on their own data? I wonder what they built on.
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
[1] https://www.businessinsider.com/thomson-reuters-builds-ai-mo...
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.
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.
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
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...
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.
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.