This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
For years, the story of AI progress has been one of moving goalposts. First, it was chess. Deep Blue beat Kasparov in 1997, and people said, fine, chess is a well defined game of search and calculation, not true intelligence. Then it was Go, which has a state space so vast it requires "intuition." AlphaGo prevailed in 2016, and the skeptics said, alright, but these are still just board games with clear rules and win conditions. "True" intelligence is about ambiguity, creativity, and language. Then came the large language models, and the critique shifted again: they are just "stochastic parrots," excellent mimics who remix their training data without any real understanding. They can write a sonnet or a blog post, but they cannot perform multi step, abstract reasoning.
I present an existence proof:
OpenAI just claimed that a model of theirs qualifies for gold in the IMO:
To be clear, this isn't a production-ready model. It's going to be kept internal, because it's clearly unfinished. Looking at its output makes it obvious why that's the case, it's akin to hearing the muttering of a wild-haired maths professor as he's hacking away at a chalkboard. The aesthetics are easily excused, because the sums don't need one.
The more mathematically minded might enjoy going through the actual proofs. This unnamed model (which is not GPT-5) solved 5/6 of the problems correctly, under the same constraints as a human sitting the exam-
As much as AI skeptics and naysayers might wish otherwise, progress hasn't slowed. It certainly hasn't stalled outright. If a "stochastic parrot" is solving the IMO, I'm just going to shut up, and let it multiply on my behalf. If you're worse than a parrot, then have the good grace to feel ashamed about it.
The most potent argument against AI understanding has been its reliance on simple reward signals. In reinforcement learning for games, the reward is obvious: you won, or you lost. But how do you provide a reward signal for a multi page mathematical proof? The space of possible proofs is infinite, and most of them are wrong in subtle ways. Wei notes that their progress required moving beyond "the RL paradigm of clear cut, verifiable rewards."
How did they manage that? Do I look like I know? It's all secret-sauce. The recent breakthroughs in reasoning models like o1 and onwards relied heavily on "RLVR", which stands for reinforcement learning with verifiable reward. At its core, RLVR is a training method that refines AI models by giving them clear, objective feedback on their performance. Unlike Reinforcement Learning from Human Feedback (RLHF), which relies on subjective human preferences to guide the model, RLVR uses an automated "verifier" to tell the model whether its output is demonstrably correct. Presumably, Wei means something different here, instead of simply scaling up RLVR.
It's also important to note that previous SOTA, DeepMind's AlphaGeometry, a specialized system, had previously achieved a silver-medal level performance and was within spitting distance of gold. A significant milestone in its own right, but OpenAI's result comes from a general-purpose reasoning model. GPT-5 won't be as good at maths, either because it's being trained to be more general at the cost of sacrificing narrow capabilities, or because this model is too unwieldy to serve at a profit. I'll bet the farm on it being used to distill more mainstream models, and the most important fact is that it exists at all.
Update: To further show that isn't just a fluke, GDM also had a model that scored Gold this Olympiad. Unfortunately, in a very Google-like manner, they were stuck waiting for legal and marketing to sign off, and OAI beat them to the scoop.
https://x.com/ns123abc/status/1946631376385515829
https://x.com/zjasper666/status/1946650175063384091
To be fair, LLMs have been moving away from this towards coding, engineering and maths because their success is easier to judge and rewards for RL-produced reasoning are easier to define.
I should have put that in quotes. I'm not that much of a wordcel apologist, even if I'm a wordcel.
True, but what I mean is that LLMs have been moving AWAY from fluid verbal intelligence and back towards the comfort zone of code and maths IMO.
I value the kind of writing ability and ‘everyday intelligence’ that the models indicated and Claude 3.7 had but I don’t think that’s the direction they’re moving in.
To an extent, they're forced to be! In a lot of mushy-mushy realms like literature, if you ask ten people to choose the "best", you'll get eleven different and mutually exclusive answers. And there's no objective way to grade between them. The closest would be RLHF, which has obvious weaknesses.
(Is JK Rowling the best living writer because she made the most money off her books? That would be a rather contentious claim. So we don't even know what to optimize for there)
I believe the hope is that there's strong expectation that there's some degree of cross-pollination, that making these models great at code, maths or physics will pay dividends elsewhere. Seems true to me, but I'm no expert.
Oh, I agree. I spent a big part of last year trying to create a personal assistant and the biggest reason for its failure was that I had no real way to judge its output.
What annoys me is that they seem to have ignored all of the ways you might optimise for this, let alone produced different products that you could trade off against each other. I would love to have one AI optimised for being lauded by literary critics, one for maximum mid-wit upvotes, etc. And you could always mix and match weights afterwards.
I am skeptical that optimising for maths and engineering ability will produce intuitive social machines because, well…
So, an interesting part of this dynamic is that sometimes expanded capabilities spill over into seemingly less related areas more than you’d think. For example, you might naively think that limiting your model to English would make it better, smaller, and faster. It does make it smaller, but actually stripping away the foreign language capabilities degrades the pure English performance! It prevents overfitting, and there’s good reason to suspect that it also improves the more nebulous “reasoning” skills. So, it’s quite possible and maybe even probable that stripping away too much of one thing might degrade the whole model, rather than allowing it to “specialize”.
More options
Context Copy link
An interesting idea. I think it's not being actively pursued because, companies like OAI don't see the economic value in such niche specialization unless it's for something as lucrative as say, producing a superhuman programmer. There's not much money in winning the Nobel Prize for Literature.
They also seem to me to be hoping that it's better to have general capabilities, and then let the user elicit what they need through prompting. If you want high-brow literary criticism, ask for it specifically, but by default, they know that mid-brow LM Arena slop and fancy formatting wins over the majority of users. Notice how companies no longer make a big deal out of the potential to make private finetunes of their models, instead claiming that RAG or search is sufficient given their flexibility and large context lengths. Which is true, IMO.
OAI did kinda-sorta half-arse personalization with their custom GPTs, but found no traction. Just the standard model becoming better made them obsolete.
Heh. Good one. However, look at Elon Musk or Zuck for examples of people who definitely lean more on technical abilities instead of people skills.
More options
Context Copy link
More options
Context Copy link
Right, LLM writing is all about preference, but I find the Chinese models relatively witty.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link