Hi Stan,
I had a look at the proofs - very impressive results! Especially because we 
had a global minimization recently, and your method found much shorter 
proofs nevertheless. 
I had a closer look at the proofs for the theorems I contributed (these are 
theorems I contributed in my first year working with Metamath, so I was 
lacking experience):

* opabbrex: reduction from 12 to 7 essential steps, using ~ssopab2
* 2eluzge0: reduction from 5 to 3 essential steps, using ~nn0uz
* elfz0add: reduction from 16 to 10 essential steps, using ~uzaddcl
* elfzmlbm:  reduction from 33 to 16 essential steps, using ~lesub1dd

Observations:
* the number of essential steps was almost halved
* the usage of only one theorem (indicated above) caused most of the 
shortening
* the proofs are still comprehensible, even better to understand because 
they are shorter

I would really appreciate if you continue your work to find more 
shortenings using OpenAI.

Alexander

On Thursday, March 26, 2020 at 5:24:02 PM UTC+1, Stanislas Polu wrote:
>
> Hi! 
>
> At OpenAI we've been exploring the use of deep learning for formal 
> math for the past couple months. We've worked a lot with Metamath 
> whose simplicity makes it very amenable to deep learning. 
>
> Today, I submitted a PR containing a sample of proofs that were found 
> by our models and that are substantially shorter than their human 
> curated counterparts: 
>
> https://github.com/metamath/set.mm/pull/1547 
>
> We're very curious to hear from the community on whether this is 
> useful. Additionally, if the authors of the original proofs shortened 
> by this PR have qualitative feedback on the proposed versions, we'd be 
> keen on hearing from them as well, obviously. 
>
> As mentioned in the PR the models were run on a sample of ~1k proofs 
> from set.mm so we could probably get 45 times as many shorter proofs 
> if there is interest for it! 
>
> Looking forward to your feedback and comments. 
>
> -stan 
>

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