The whole "mystery" of transformer is that instead of a linear sequence of static weights times values in each layer, you now have 3 different matrices that are obtained from the same input through multiplication of learned weights, and then you just multiply the matrices together. I.e more parallelism which works out nice, but very restrictive since the attention formula is static.
We arent going to see more progress until we have a way to generalize the compute graph as a learnable parameter. I dunno if this is even possible in the traditional sense of gradients due to chaotic effects (i.e small changes reflect big shifts in performance), it may have to be some form of genetic algorithm or pso that happens under the hood.
We arent going to see more progress until we have a way to generalize the compute graph as a learnable parameter. I dunno if this is even possible in the traditional sense of gradients due to chaotic effects (i.e small changes reflect big shifts in performance), it may have to be some form of genetic algorithm or pso that happens under the hood.