alt Hacker News> extra attention head were added that queried the KV data from lower layers
Isn't this sort of similar to latent looping? E.g. [1]. But actually as [2] argues, even that wasn't a good experiment because it used the very last hidden state, which is too close to the logits and loses most of the rich embedding structure. Perhaps you don't even need access to the state of anything except the penultimate hidden layer, since based on my vague reading of [3] the residual stream doesn't "lose information" as it passes deeper down the attention layers, so each block maybe manipulates a different subspace of the residual stream.
[1] https://arxiv.org/abs/2412.06769
[2] https://snimu.github.io/2025/03/30/multi-layer-language-head...
> Perhaps you don't even need access to the state of anything except the penultimate hidden layer, since based on my vague reading of [3] the residual stream doesn't "lose information" as it passes deeper down the attention layers, so each block maybe manipulates a different subspace of the residual stream.
I imagine that conventional transformers kind of force this. If you train a transformer such that it needs to learn the ability to do tasks like “Repeat the following words: apple banana cat” then the model is sort of forced to internally propagate the input far enough along to be able to perform the task. But maybe if you pre-trained from scratch with an architecture where later layers get direct access to earlier layers and/or the raw input, then the model wouldn’t need to propagate information.
Or maybe it would all fall apart and something would go wrong with the gradients.