So its a model designed exclusively for a purpose? Then the results should not be that surprising. It's still impressive don't get me wrong.

Anybody have a link to this model?

Can't seem to see it on the arxiv site.

It really seems like all the next big leaps in AI are going to be fine-tuning fit-for-purpose models.

Everything past GPT5 has been ... fine. It's better at chat (sort of, depending on your tone preferenc) and way better at coding/tool use. In our product (plan out a migration with AI), they've gotten worse, because they want to chat or code. I'd have expected the coding knowledge to generalize, but no! Especially Claude really wants to change our code or explain the existing plan to me.

We're getting around it with examples and dynamic prompts, but it's pretty clear that fine-tuning is in our future. I suspect most of the broad-based AI success is going to look like that in the next couple years.

LLMs are only going to improve by fragmenting them into specialized systems for low parameter high performance results. We’ve reached the point where models will get smaller and more compact

We're seeing the insect-ization of neural nets. Smaller specialists are evolving for their relevant tasks

Sceptical. Performs really well on 1000 docs. Let’s see it for real..! No model supplied.

https://github.com/herniqeu/extract0

To quote Mulder: I want to believe.

Seems this gives us a clear bifurcation of what AI is about to do.

Open-Source style small players will actually solve problems with AI.

And the big money invested things are going to do stupid pointless bubbly things at best, or enshittify other good things at worst.

Govern yourselves accordingly.

It's not surprising given specialization typically leads to better outcomes.

I guess this is a small step forward, if nothing else, to the day when I can actually teach a model something in situ on my personal machine (notice I said machine, not "machines") in a very short amount of time. I feel that until then, LLMs and similar technologies won't be maximally helpful. They're very useful, but not maximally helpful.

It's wild to me how many people still think that fine-tuning doesn't work. There was just a thread the other day with numerous people arguing that RL should only be done by the big labs.

There is so much research that shows you can beat frontier models with very little investment. It's confusing that the industry at large hasn't caught up with that

A LoRA fine tune of DeepSeekR1-Distill-Qwen-7B with a training cost of $196.

Ok, but what was the cost of labor put into curation of the training dataset and performing the fine-tuning? Hasn’t the paper’s conclusion been repeatedly demonstrated - that it is possible to get really good task-specific performance out of fine-tuned smaller models? There just remains the massive caveat that closed-source models are pretty cheap and so the ROI isn’t there in a lot of cases.

> the generation of 281,128 augmented examples, from which 1,000 were held out as a benchmark test set.

This model is trained on a custom dataset of 280k examples then tested on 1k very similar examples from the same dataset. Of course it is specialized to outperform general models on this specific task in this specific domain with this specific json format for output.

This is a reasonable hobby project and interesting approach to synthetic data generation but not impressive research.

At minimum you should test your model on other benchmarks that have similar tasks e.g. docbench

The LoRA + GRPO training pipeline and the semantic similarity reward function over exact matching is actually interesting, but there is an evaluation issue that is a significant problem if you want to accept the headline at face value.

They trained on synthetic extractions like "extract equations from arXiv papers" and "extract regulatory information from FDA documents," then tested on more synthetic extractions from the same sources. Essentially, "model trained on synthetic arXiv/PubMed/FDA extractions performs better on more synthetic arXiv/PubMed/FDA extractions than a model that never saw this distribution."

I'd like to see how it handles extractions from a real contract, or a low quality scan of a financial document, or processes a format it didn't see in training. o3 very likely handles these variations better, but we don't have that data to compare.

We need the model weights or tests on standard benchmarks to verify if this generalizes beyond documents that look like the training distribution.