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

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

#31
post #3

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

The "obvious" thing to try, which presumably some people are trying pretty hard right now[1], is to (1) use a mathematically-tuned LLM like this one to propose informal Next Things To Try, (2) use an LLM (possibly the same LLM) to convert those into proof assistant formalism, (3) use the proof assistant to check whether what the LLM has suggested is valid, and (4) hook the whole thing together to make a proof-finding-and-verifying machine that never falsely claims to have proved something (because everything goes through that proof assistant) and therefore can tolerate confabulations from LLM #1 and errors from LLM #2 because all those do is waste some work.

[1] IIRC, AlphaProof is a bit like this. But I bet that either there's a whole lot of effort on this sort of thing in the major AI labs, or else there's some good reason to expect it not to work that I haven't thought of. (Maybe just the "bitter lesson", I guess.)

It would doubtless be challenging to get such a system to find large difficult proofs, because it's not so easy to tell what's making progress and what isn't. Maybe you need LLM #3, which again might or might not be the same as the other two LLMs, to assess what parts of the attempt so far seem like they're useful, and scrub the rest from the context or at least stash it somewhere less visible.

It is, of course, also challenging for human mathematicians to find large difficult proofs, and one of the reasons for them is that it's not so easy to tell what's making progress and what isn't. Another major reason, though, is that sometimes you need a genuinely new idea, and so far LLMs aren't particularly good at coming up with those. But a lot of new-enough-ideas[2] are things like "try a version of this technique that worked well in an apparently unrelated field", which is the kind of thing LLMs aren't so bad at.

[2] Also a lot of the new-enough-ideas that mathematicians get really happy about. One of the cool things about mathematics is the way that superficially-unrelated things can turn out to share some of their structure. If LLMs get good at finding that sort of thing but never manage any deeper creativity than that, it could still be enough to produce things that human mathematicians find beautiful.

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

#32
post #22

The core innovation is a verifier-generator dual architecture that enables the model to self-check reasoning rigor, addressing the fundamental problem that correct answers don't guarantee correct reasoning processes.

The thing that stands out is fine-tuning a verifier with human labels specifically so that it isn't sycophantic in either direction. If you've ever tried to do a verifier in a multi-agent system you'll recognize the annoyance of the verifier swinging wildly from "this is brilliant" to "this is trash" based on nothing more than fudging a few suggestive words in the candidate answer it's tasked with reviewing. Making the verifier invariant to those fudge words and forcing it to actually reason (... as per Anthropic's interpretability work) would be quite nice.

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

#33
Amazing model! I'm trying to get it to run on an ec2 machine right now, but it looks like a lot of the performance actually depends on more than just classical LLM inference. And it looks like Deepseek didn't share their scripts to do the parallel thinking traces and self-verification loops. Is anybody else working on recreating this right now?

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

#34
post #21

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).

Also the impressive IMO-ProofBench Basic benchmark, the model achieved nearly 99% accuracy, though it fell slightly behind Gemini Deep Think on the Advanced subset. The approach shifts from "result-oriented" to "process-oriented" verification, particularly important for theorem proving where rigorous step-by-step derivation matters more than just numerical answers.

"Process-oriented" verification has been a thing for a while in mathematical reasoning CoT. Google had a paper about it last year [1]. The key term to look for is "Process-reward model." I particularly like RL Tango [2].

[1] https://arxiv.org/abs/2406.06592

[2] https://arxiv.org/abs/2505.15034

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

#36
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.

> 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.

Turing machines are also deterministic, but there is no algorithm that can decide whether any given Turing machine halts. What you're asking for is a solution to the Halting Problem.

That's the first problem, the second problem is that any such system that didn't support natural language would require a formal language of some sort, and then you would have to convince every mathematician to write their proofs in your language so it can be checked. All attempts at this have failed to gain much traction, although Lean has gotten pretty far.

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

#37
post #26

Earlier quoted context omitted.

It doesn't. However, having a free-of-charge maths genius available 24/7 has broad potential. It's hard to predict what it will be used for.

It would be helpful in automating the busy work of many verification aware programming languages. At least the Dafny authors are excited about it.

Another possibility is to automatically annotate a software with assertions, preconditions, postconditions or other verification annotations based on the languages semantics and programmer intent, and then run a verifier on the result and evolve the program and annotations based on that intent. So for C, it could fill in data needed by Frama-C.

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

#38
post #26
post #23

How this improvement translate into real world agentic coding task ?

It doesn't. However, having a free-of-charge maths genius available 24/7 has broad potential. It's hard to predict what it will be used for.

This already exists: https://www.wolframalpha.com/

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

#39

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).

I think serious math research progress should come in 1-2 years. It basically only depends on how hard informal verification is, because training data should be not a problem and if informal verification is easy you can throw RL compute at it until it improves.

LLMs are already a powerful tool for serious math researchers, just not at the level of "fire and forget", where they would completely replace mathematicians.
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