‘The Expressive Power of Transformers with Chain of Thought’ is extremely interesting, thank you! I’ve noticed a tendency to conflate the limitations of what transformers can do in a forward pass with what they can do under autoregressive conditions, so it’s great to see research explicitly addressing how the latter extends the former.
the “bugs” we see in their behavior could be addressed through manageable algorithmic changes + a few OOMs more of compute...I think scaling + a little creativity is alive and well as a pathway to nearish-term AGI.
I agree that this is plausible. I mentally lumped this sort of thing into the ‘breakthrough needed’ category in the ‘Why does this matter?’ section. Your point is well-taken that there are relatively small improvements that could make the difference, but to me that has to be balanced against the fact that there have been an enormous number of papers claiming improvements to the transformer architecture that then haven’t been adopted.
From outside the scaling labs, it’s hard to know how much of that is the improvements not panning out vs a lack of willingness & ability to throw resources at pursuing them. One the one hand I suspect there’s an incentive to focus on the path that they know is working, namely continuing to scale up. On the other hand, scaling the current architecture is an extremely compute-intensive path, so I would think that it’s worth putting resources into trying to see whether these improvements would work well at scale. If you (or anyone else) has insight into the degree to which the scaling labs are actually trying to incorporate the various claimed improvements, I’d be quite interested to know.
‘The Expressive Power of Transformers with Chain of Thought’ is extremely interesting, thank you! I’ve noticed a tendency to conflate the limitations of what transformers can do in a forward pass with what they can do under autoregressive conditions, so it’s great to see research explicitly addressing how the latter extends the former.
I agree that this is plausible. I mentally lumped this sort of thing into the ‘breakthrough needed’ category in the ‘Why does this matter?’ section. Your point is well-taken that there are relatively small improvements that could make the difference, but to me that has to be balanced against the fact that there have been an enormous number of papers claiming improvements to the transformer architecture that then haven’t been adopted.
From outside the scaling labs, it’s hard to know how much of that is the improvements not panning out vs a lack of willingness & ability to throw resources at pursuing them. One the one hand I suspect there’s an incentive to focus on the path that they know is working, namely continuing to scale up. On the other hand, scaling the current architecture is an extremely compute-intensive path, so I would think that it’s worth putting resources into trying to see whether these improvements would work well at scale. If you (or anyone else) has insight into the degree to which the scaling labs are actually trying to incorporate the various claimed improvements, I’d be quite interested to know.