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- Hacker News
- Cool that they did this on top of Qwen3.6-35B-A3B. If they have their own collection of valuable data this is the only way to make sure it doesn’t end up in general purpose models. That’s probably enough justification for the $40m spend - continued control of your destiny as an information provider.by JSR_FDED
- > Our evaluation found Thomson’s citation quality generally competitive with leading frontier models, even when tested on Canadian employment-law questions without a Canada-specific setting.
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?
by elpakal - Pretty cool someone is still doing this. Training in house LLMs was extremely popular in 2023-2024, back when domain-specific LLMs could easily top GPT in their field. In my field alone (tax/HR tech) I remember that Intuit, Workday, Indeed, LinkedIn were all training internal models.
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.
by x313 - Maybe it's my dyslexic brain but "it's own frontier model" in my head converted to foundational model that was trained from scratch.
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
by scirob - > starting from a strong open-source foundation and investing $40 million to train Thomson
Sounds like they spent $40 million finetuning an open weight model on their own data? I wonder what they built on.
by Arcuru - Here is some more technical information on how this was trained, as well as a download link.
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
(Full disclosure I’m a TR employee, although I had nothing to do with making this)
by johnnypangs - This is going to increasingly happen over the years to come. Big organizations will become more sophisticated with operationalizing their data, training and running LLMs will continue to be demystified and accessible, and over time we'll get more and more specialized / industry-specific models.
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.
by cootsnuck - Please challenge me and explain why this is a break through worth the headline they use for their own work.
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"
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