DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning
(huggingface.co)263 points by victorbuilds 2 days ago
263 points by victorbuilds 2 days ago
I think we should treat copyright for the weights the same way the AI companies treat source material ;)
We don't even have to do that: weights being entirely machine generated without human intervention, they are likely not copyrightable in the first place.
In fact, we should collectively refuse to abide to these fantasy license before weight copyrightability gets created out of thin air because it's been commonplace for long enough.
There's an argument by which machine-learned neural network weights are a lossy compression of (as well as a smooth interpolator over) the training set.
An mp3 file is also a machine-generated lossy compression of a cd-quality .wav file, but it's clearly copyrightable.
To that extent, the main difference between a neural network and an .mp3 is that the mp3 compression cannot be used to interpolate between two copyrighted works to output something in the middle. This is, on the other hand, perhaps the most common use case for genAI, and it's actually tricky to get it to not output something "in the middle" (but also not impossible).
I think the copyright argument could really go either way here.
Of course we should! And everyone who says otherwise must be delusional or sort of a gaslighter, as this whole "innovation" (or remix (or comopression)) is enabled by the creative value of the source product. Given AI companies never ever respected this copyright, we should give them similar treatment.
If they open source just weights and not the training code and data, then it’s still proprietary.
You can distill closed weights models as well. (Just not logit-distillation)
Isn't that a bit like saying that if I open source a tool, but not a full compendium of all the code that I had read, which led me to develop it, then it's not really open source?
Yes, I 100% agree. Open Source is a lot more about not paying than about liberty.
This is exactly the tradeoff that we had made in the industry a couple of decades ago. We could have pushed all-in on Stallman's vision and the FSF's definition of Free Software, but we (collectively) decided that it's more important to get the practical benefits of having all these repos up there on GitHub and us not suing each other over copyright infringement. It's absolutely legitimate to say that we made the wrong choice, and I might agree, but a choice was made, and Open Source != Free Software.
https://www.gnu.org/philosophy/open-source-misses-the-point....
No. In that case, you're providing two things, a binary version of your tool, and the tool's source. That tool's source is available to inspect and build their own copy. However, given just the weights, we don't have the source, and can't inspect what alignment went into it. In the case of DeepSeek, we know they had to purposefully cause their model to consider Tiananmen Square something it shouldn't discuss. But without the source used to create the model, we don't know what else is lurking around inside the model.
No, it's like saying that if you release under Apache license, it's not open source even though it's under an open source license
For something to be open source it needs to have sources released. Sources are the things in the preferred format to be edited. So the code used for training is obviously source (people can edit the training code to change something about the released weights). Also the training data, under the same rationale: people can select which data is used for training to change the weights
Previous discussion: https://news.ycombinator.com/item?id=46072786 218 points 3 days ago, 48 comments
It's impressive to see how fast open-weights models are catching up in specialized domains like math and reasoning. I'm curious if anyone has tested this model for complex logic tasks in coding? Sometimes strong math performance correlates well with debugging or algorithm generation.
kimi-k2 is pretty decent at coding but it’s nowhere near the SOTA models of Anthropic/OpenAI/Google.
Are you referring to the new reasoning version of Kimi K2?
Shouldn’t there be a lot of skepticism here?
All the problems they claim to have solved are on are the Internet and they explicitly say they crawled them. They do not mention doing any benchmark decontamination or excluding 2024/2025 competition problems from training.
IIRC correctly OpenAI/Google did not have access to the 2025 problems before testing their experimental math models.
Why isn’t OpenAI’s gold medal-winning model available to the public yet?
A bit important that this model is not general purpose whereas the ones Google and OpenAI used were general purpose.
Both OpenAI and Google used models made specifically for the task, not their general-purpose products.
OpenAI: https://xcancel.com/alexwei_/status/1946477756738629827#m "we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months."
DeepMind: https://deepmind.google/blog/advanced-version-of-gemini-with... "we additionally trained this version of Gemini on novel reinforcement learning techniques that can leverage more multi-step reasoning, problem-solving and theorem-proving data. We also provided Gemini with access to a curated corpus of high-quality solutions to mathematics problems, and added some general hints and tips on how to approach IMO problems to its instructions."
https://x.com/sama/status/1946569252296929727
>we achieved gold medal level performance on the 2025 IMO competition with a general-purpose reasoning system! to emphasize, this is an LLM doing math and not a specific formal math system; it is part of our main push towards general intelligence.
asterisks mine
Do note that that is a different model. The one we are talking about here, DeepSeekMath-V2, is indeed overcooked with math RL. It's so eager to solve math problems, that it even comes up with random ones if you prompt it with "Hello".
That's a different model: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
Oh you may be correct. Are these models general purpose or fine tuned for mathematics?
Does anyone know if this will become available on OpenRouter?
How is OpenAI going to be able to serve ads in chatgpt without everyone immediately jumping ship to another model?
I suppose the hope is that they don’t, and we wind up with commodity frontier models from multiple providers at market rates.
The same way people stayed on Google despite DuckDuckGo existing.
by having datacenters with GPUs and API everyone uses.
So they are either earning money directly or on the API calls.
Now, competition can come and compete on that, but they will probably still be the first choice for foreseeable future
and now, when they are not, everyone else's results are also pretty terrible...
Notable: they open-sourced the weights under Apache 2.0, unlike OpenAI and DeepMind whose IMO gold models are still proprietary.