Matching neartermist beliefs

 

Maybe remove those

OR Simultaneous appearance model

Add legend, add a one page summary at the start.

Any examples of where the starting resource allocation has more than 1 solution/equilibrium, assuming demand independence is violated?

puted as the derivate of $C{S}_w$ by $\%D$, $w$ stands for World. We derive with respect to $\%D$ (i.e., a proportional change in SFC density) because our causal impact is best modeled as shifting the probability that Humanity creates an SFC, and we impact the log odds of that probability.

Make sure its clear and correct. Maybe D at the end is bellow

Formalization

Make an one-page summary before that

The way the isoFLOPs are shaped suggests that 90-95% sparsity might turn out to be optimal here, that is you can only get worse loss with 98+% sparsity with 1e20 FLOPs, however you vary the number of epochs and active params!

I'm currently skeptical and more minimally, I don't understand the argument you're making. Probably not worth getting into.

I do think there will be a limit to how sparse you want to even in the very high compute relative to data regime for various reasons (computational if nothing else). I don't see how these graphs support 90-95% sparsity, but I had a hard time understanding your argument.

Regardless, I don't think this argues against my claim, not sure if you were trying to argue against the claim I was saying or add context. (Insofar as your argument is true, it does limit the returns from MoE in the regime with little data.)

Reframing anthropic theories as ADT implementations

 

One-Page summary to add before that

Thanks for the feedback! I was quite surprised at the Claude results myself. I did play around a little bit with the prompt on Claude 3.5 Sonnet, and found that it could change the result on individual questions, but I couldn't get it to change the overall accuracy much that way -- other questions would also flip to refusal. So this certainly warrants further investigation, but by itself I wouldn't take it as evidence the overall result changes .

In fact, a friend of mine got Claude to answer questions quite consistently, and could only replicate the frequent refusals when he tested questions with his user history disabled. It's pure speculation, but the inconsistency on specific questions makes me think this behaviour might be caused by reward misspecification and not intentionally trained (which I imagine would result in something more reliable).

Chinchilla scaling shows that tokens/params ratio for compute optimal models only changes slowly with compute, making it a good anchor to frame other things in terms of. The experiments from this MoE scaling paper show that under fixed data, varying sparsity in MoEs that are compute optimal at that amount of data preserves perplexity. This also seems like a nice principle for framing the way compute optimal models sit in the space of hyperparameters.

With infinite data, isoFLOPs for loss depending on number of active params are parabolas with some minimum point. But with finite data you need to repeat it to train with fewer active params, which damages loss. This moves the minima of isoFLOPs to the right if the minima already required 5x repetition or more. So under data scarcity, compute optimal models have more active params than under infinite data, and the effect gets worse with more compute. This way we maintain the framing of search for compute optimal hyperparameters rather than undertraining.

Now consider the 1e20 FLOPs plot in Figure 12, left. If there's only 2B tokens of training data and no more, all minima already ask for 12-31 epochs, so the distortion that increases loss will move the minima to the right (and up), and move the high sparsity minima further than lower sparsity minima compared to their original (infinite data) locations. The way the isoFLOPs are shaped suggests that 90-95% sparsity might turn out to be optimal here, that is you can only get worse loss with 98+% sparsity at 1e20 FLOPs, however you vary the number of epochs and active params! This seems counterintuitive, as in an infinite data regime more sparsity only makes things better (if we ignore practical difficulties). But sure, 90% sparsity will still be better than dense, at least until we use even more compute and sparser minima start asking for even more epochs.