alt Hacker NewsIf you think about it for some time then you’ll come to realise transformers are autoencoders on steroids. A small input space is expanded onto a big manifold and contracted again. Now, suppose you want to impose a function to regulate the output of an autoencoder. It’s actually pretty obvious that you need exactly one layer to do so… f(manifold).
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What you're suggesting seems to go implausibly far beyond what the paper says.
RL post-training alters the parameters of the transformer, while your f(manifold) idea seems to suggest that a new layer on top would suffice, no need to alter the transformer itself at all.
It would be extremely handy if that were so, but I'm guessing it isn't, or it would be the prevailing approach.
Took me a short time to understand what you mean with "autoencoders on steroids", but I believe you mean they are autoencoders with an inverse bottleneck - an intermediate representation that isn't smaller, but that's much larger than the input space. Is my understanding of your comment correct?
I might be misunderstanding your point but this conflates the distinguishing features of each. you mention expansion but autoencoders canonically compress their inputs. autoencoders have an explicit encoder and decoder. most transformers we interact with these days (LLMs) are decoder only. the manifold isn't typically something the model is applied to directly. we apply the function/model to the latent representations. those are what live on the manifold.
Everything can be represented as f(), a full scale SotA transformer model is also just f(context). That does not mean one layer is sufficient. It all depends on the level of expressivity required by this f to be a good model.