I actually disagree (and I'll also note that I don't like the term "emergent"). There's a few factors that are coupled with the analysis that matter here.

Re: Metrics

I don't think Wei is wrong about what he's said here but has responded to a rather weak form of the argument. It is correct that we, in the end, care about a binary distinction of getting the answer right vs wrong. But the issue here is that with hard metrics we have a very flat loss landscape and so there is little information being fed back to the network. You are perfectly capable of combining hard and soft metrics or even having the soft metrics decay or turn off after sufficient learning. I'm not aware of anyone that's explored this, but it is a natural hypothesis that we should have fairly high confidence that this would result in good results considering we already see smooth performance in soft metrics. Similarly it should be unsurprising that a hard metric has jumps.

The larger models have a clear advantage here not just via data but because the number of parameters allows the model to fold/unfold the data through means that the smaller model couldn't and so the bigger question is about if the smaller model could learn such foldings given sufficient time. In other words, larger models can simply search a large solution space faster, so if it's takes a random hit to find a minima, the expectation that a large model finds one is going to be exceptionally higher than that for a small model.

The idea here is also more abstract than his critique that cross-entropy on IPA transliterate still has a large kink because the ultimate question here is about how flat the loss landscape is and our expectation of stumbling upon non-flat regions. I simply would not expect smooth gains if our loss space (via metric or even via the problem itself) is flat with sparse optima.

Re: UShape

This is certainly a surprising phenomena and worthy of investigation. But I think it is also not clearly dismissed via the above framework. The losses need not be perfectly flat and as any good mathematician knows, a metric can lead you in the wrong direction if used wrong enough. I'm not saying this validates my claim above but rather that it doesn't invalidate it. If in fact the landscape is a very soft slope pointing away from a deep optima (think approaching a volcano, but a very soft grade) can result in this phenomena. This would be a very tough optimization problem but we do have many more opportunities to find the magma chamber with a large model.

This also ties into chain of thought with essentially the same reasoning he gives. But I've always thought of chain of thought prompting as a bit of cheating. It's incredibly easy to introduce information leakage into a model and COT is often giving hints to your models. I do find a certain irony here given that he critiqued soft metrics earlier.

I don't know if I'm wrong or right. But I do certainly think it is too early to dismiss the ideas. I do think we really do need to get more into (read advance) model interpretability to even approach these questions in a good way. I also think our community needs to stop shying away from math.

> Focusing on metrics that best measure the behavior we care about is important because benchmarks are essentially an “optimization function” for researchers.

I also want to address this, despite it being in the first Re. I will continue to rage against this idea, even if softly put in quotes. No metric is anywhere near close to the behaviors we actually care about. There are no metrics for quality of speech, visual fidelity, vocal realism, and so on. It's impressive that we've done so well when you dig into the metrics we use, but they were selected with care. At the same time, benchmarks are highly limited and especially in discussions of large models (language or vision) these metrics and benchmarks are showing their limitations. Simply due to the alignment of said metrics with desired behavior. We don't desire that the distribution that the LLM learned is indistinguishable from the distribution we used to train it (KL -> 0), but rather we desire that a LLM is able to write language well and perform complex tasks. These do relate, but they are not the same thing. It also makes it disingenuous to compare models with different training sets (such as comparing a JFT pretrained model to something else) due to the nature of what we're actually measuring (e.g. JFT may very well, and likely is, a better approximation of the desired object we're modeling with probability distributions and tuning it to have similar distributional properties to a subset distribution is far easier than training something to learn a distribution in the first place). The desired outcome is, as best we can tell, ineffable. It takes far more than a metric and a benchmark (or several) to quantify the performance of even simple models, let along these beautiful lovecraftian constructs.