rsfern

5 ms·

1.
by rsfern 6d ago
According to OpenAI, but they haven’t exactly been transparent about what information the prompt entailed. The bigger question is to what extent did expert mathematicians metaprompt the model with fruitful solution strategies through their
2.
by rsfern 7d ago
I think that’s a lot of risk of anchoring reviewer bias. I’d be more comfortable with a triaged review where the editor’s office uses models to score whether a human editor should evaluate a paper to potentially send out for review, then th
3.
by rsfern 9d ago
For me the convenience of “hey remember this fact” is outweighed by a desire not to get stuck in a search or context bubble. It might be nice to have better UI to control which bits of history get added to the context of a chat, but then ju
4.
by rsfern 9d ago
It should resonate here. The thesis is that good design principles are transferable between humans and agents. So focus on solving important problems and build well designed tools and documentation to get there, you don’t need special desig
5.
by rsfern 10d ago
There’s no reason it has to be like that. We could change course one more time back to a stable and non-partisan science funding landscape and then stick with it if we choose to. Even some of the alarm in this article over the proposed fund
6.
by rsfern 10d ago
On the contrary, I think the chess comparison is on point. We’re discussing observations that even the strongest models devolve into making invalid moves without scaffolding. For me that raises the question of whether these models are learn
7.
by rsfern 10d ago
Maybe it depends on the person? My six year old isn’t great at strategy but they can pretty consistently make valid moves. Sometimes they ask for confirmation on a move which is also not a trait I see in language models (at least unprompted
8.
by rsfern 11d ago
the discussion isn’t really about whether language models can become strong chess players though, the point is they seem to struggle to consistently make valid moves. Most humans don’t need to read two books to pick that up, just a couple l
9.
by rsfern 12d ago
For numerical code I like einops.reduce more than numpy/pytorch sum reductions because you can reduce over named dimensions. It’s much more readable than having to reason through axis indexing again every time you come back to the code
10.
by rsfern 16d ago
I agree (and so does Buckmaster based on his written statement) that we are better having solved this. But I disagree that which humans were credited is the heart of the issue in this particular controversy. The question is what do you need
11.
by rsfern 16d ago
That’s the prevailing narrative, but I think this controversy calls it into question to some extent. If the OpenAI result wouldn’t have been possible without experts seeding the training data with feedback on promising solution routes, ther
12.
by rsfern 16d ago
I think you’re missing an important distinction. “Major damage” to the talent pipeline because models become capable of original end-to-end mathematics is what the community has been discussing. But if the models rely on sniping nearly comp
13.
by rsfern 16d ago
The session data could be cryptographically signed. Probably easier in an open harness?
14.
by rsfern 17d ago
The screen cap of their full correspondence in the Twitter thread, which was the subject of the preceding sentence. The Twitter post has an obviously incomplete fragment of the conversation that doesn’t resolve what the author presents it a
15.
by rsfern 17d ago
Without seeing the full correspondence it’s hard to evaluate for sure, but parent linked to a tweet from the OpenAI employee at the center of the controversy, that’s a primary source you can read and evaluate yourself Personally I don’t fin
16.
by rsfern 17d ago
It does, yes. So designing objections functions and making sure you can afford the training rollouts becomes really important in defining which problems are tractable. It will be really interesting to see how that shapes the kinds of proble
17.
by rsfern 17d ago
Agreed, but i think this underscores my point. We have numerical simulations in materials science too, but that doesn’t mean formally verified theorems about the underlying equations automatically translate to formal (or even informal) veri
18.
by rsfern 17d ago
Yes, definitely! There’s a long history of this and I think there’s tons of opportunities for more. Both for improving the exactness/physical fidelity of models and for developing new approximate theories and simulation methods
19.
by rsfern 17d ago
Regardless of what you think of the priority dispute issue discussed on sibling threads, I’m highly skeptical of the closing quote that this Navier Stokes result means that the same approach of casually spending a few million on agentic com
20.
by rsfern 17d ago
Thanks! This seems really cool. If I’ve got it right, your UI builds and displays these diffs (and implements undo/redo) by parsing the edit tool calls? If so that seems really nice, one of the things I don’t like with coding agents is
21.
by rsfern 18d ago
Interesting project idea! The link seems to be 404, is the repo still private?
22.
by rsfern 18d ago
Why would mining chat transcripts for ideas be untenable? They already run a summarization model to auto-title the chat, and to run a bunch of safety filters, and presumably to score transcript quality for A/B testing and to collect mo
23.
by rsfern 2mo ago
The bit you quoted doesn’t capture why the reporters are upset: > White House journalists are outraged that a threat credible enough to force Donald Trump to escape from Air Force One using an airport catering truck wasn’t relayed to th
24.
by rsfern 2mo ago
Right, I did specifically say that most of the DOE scientists are contractors, but I concede the phrase “government scientist” is a bit ambiguous. I appreciate the extra detail you added. I think the distinction between political appointee
25.
by rsfern 2mo ago
Let’s distinguish a bit. There are political appointees (Trump’s government employees as you say) who are mostly upper management, and there are career civil servants (all the government scientists are under this category) who have a strong
26.
by rsfern 2mo ago
I found this paper really thought provoking, but I think the conclusion of “world models are the solution” leaves something to be desired. People are already equipping agentic systems with physical simulation tools and exploring action-cond
27.
by rsfern 2mo ago
Or they could store the reading traces and validate the user hasn’t edited them server-side? They could sign reasoning traces so they can’t be counterfeited?
28.
by rsfern 2mo ago
The cell DAG enforces that there’s no implicit state, which reduces cognitive load for me a lot and provides some pressure to abstract experimental code into functions. In Jupyter this is left to user discipline and restart-and-run-all work
29.
by rsfern 2mo ago
My point with the force field example wasn’t to argue against neural scaling as a valid strategy, it totally is effective and a lot of groups are doing it. But I feel like we might be talking past each other a bit. What I’m pushing back on
30.
by rsfern 2mo ago
I don’t think there’s a fundamental reason that performance has to be monotonic in model size or even training FLOPs. At least I don’t think it’s been proved to be so, so I think “misinformed” is a bit premature and sort of makes GP’s point

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