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DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

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11–20 of 53 posts

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#11
post #4

Something weird here, why is it so hard to have a deterministic program capable of checking a proof or anything math related, aren't maths super deterministic when natural language is not. From first principles, it should be possible to do this without a llm verifier.

Maths can be super deterministic but often difficult to compute because of concepts like inferring by induction. I had to personally unlearn and rebase my understanding of math based in computation to 'get' pure maths. Another example is set building. You often don't need to compute the existence of members of sets in pure math you just need to agree that there are some members of a set that meet the criteria. How many or how many things that aren't in the set aren't meaningful often times to accept something and move on with the proof. From the computing perspective this can be difficult to put together.

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#12
That is amazing if they can do all of this at < 10 % of the cost of frontier labs. Off course they work in the shadows of the great work done in the frontier labs and shared, but there is some exceptional high speed execution happening behind the scenes that shows this is clearly a race, but a race where China is happy to be #2 as long as the gap is not significant and the costs are reasonable

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#13
Is everyone just glossing over the first place score of 118/120 on the Putnam?! I mean we'll see how it does on the upcoming 2025 test, but that's insane!

We've seen absolutely ridiculous progress in model capability over the past year (which is also quite terrifying).

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#15
post #3

It's cool, but I genuinely cannot fathom why they are targeting natural language proofs instead of a proof assistant.

Well they do that too: https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-671B

But I suppose the bigger goal remains improving their language model, and this was an experimentation born from that. These works are symbiotic; the original DeepSeekMath resulted in GRPO, which eventually formed the backbone of their R1 model: https://arxiv.org/abs/2402.03300

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#16
post #3

It's cool, but I genuinely cannot fathom why they are targeting natural language proofs instead of a proof assistant.

More training data on advanced math. Lean is cool, but it's mostly about formalizing stuff we already know.

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#17
post #4

Something weird here, why is it so hard to have a deterministic program capable of checking a proof or anything math related, aren't maths super deterministic when natural language is not. From first principles, it should be possible to do this without a llm verifier.

Verifying math requires something like Lean which is a huge bottleneck, as the paper explains.

Plus there isn't a lot of training data in lean.

Most gains come from training on stuff already out there, not really the RLVR part which just amps it up a bit.

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#18

So it's designed for informal proofs and it "verifies" based on a rubric fitting function and human interaction, is that right? What's the use case for a system like this?

Advanced math solving, as the results indicate. Informal proof reasoning is advancing faster than formal proof reasoning because the latter is slow and compute intensive.

I suspect it's also because there isn't a lot of data to train on.

Re: DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning [pdf]

#19
post #14

If i read it right it used multiple samples of itself to verify the aqccuracy, but isnt this problematic?

Problematic in that it's still not formal verification, not problematic as in "it's worse to do this than not".
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