kevinwang
I certainly think you could say both of those are reasoning in latent space. But of course people can have different definitions of what they consider reasoning. Maybe this is because I think it's hard to pin down what "reasoning" is so I instead alias it to "computation" which both of those examples do.
Isn't this an ironic example to use, since one of the only lower bounds we have actually found is nlogn for comparison-based sorting?
And anyways, this point seems too strong: the fact that TCS people believe NP != P is another example that we believe a lower bound exists, no? (And relatedly, the large portion of TCS that is dedicated to trying to figure out how to lower-bound anything, while admittedly not so successful yet, shows that researchers believe lower bounds exist. I mean, any complexity class non-equality hypothesis is an example of this, no?)
> 32-bit XorShift should usually not be used to produce 32-bit numbers, because it only produces each number once, and never produces zero.
(From this page I found while trying to see if this was a common flaw in PRNGs: https://www.pcg-random.org/other-rngs.html )
The other two can be simply expressed as a list comprehension, but afaik you can't with reduce (and if you can, it's probably awful).
As a tangent, it reminds me a bit of cryptocurrency: in the earliest days the interested parties have a mix of intellectual (can it be done? How can it be done?) and some idealistic (this technology will have utility, like reducing spam or libertarianism for crypto, or giving us a more accurate and precise understanding of the world for prediction markets) values. Of course, as both are directly financial, the third value is that one stands to get rich off of the technology. This third value might be adequately communally suppressed in the beginning, but the force of money gradually makes this third value the only one that matters, and both technologies become net-negatives on humanity.
So maybe that's something like: the same model is trained in multi-agent scenarios (e.g. the prompt says that you are working together, or that you have some specific role, and access to communication tools, and you have some common objective, and the reward signal is some combination of collectively performing the task + some reward-shaping reward that rewards collaboration) and also in single-agent scenarios. They expect that this makes the agents good at working together when it's in such a multi-agent setting, but unintendedly it also became very eager to work together in the single-agent settings as well. Just my speculation.
And was the desire to communicate, specifically over this specific message board, reinforced into the model parameters over the course of the this training run?
Ah, yeah. That is what I meant. Thanks!
Curious what this is!