Sure, but the interesting thing to me isn’t fixed points in the input/output map, it’s properties (i.e. attractors that are allowed to be large sets) that propagate from the answers seen by a human in response to their queries, into their output.
Even if there’s a fixed point, you have to further prove that this fixed point is consistent—that it’s actually the answer to some askable question. I feel like this is sort of analogous to Hofstadter’s q-sequence.
In the giant lookup table space, HCH must converge to a cycle, although that convergence can be really slow. I think you have convergence to a stationary distribution if each layer is trained on a random mix of several previous layers. Of course, you can still have occilations in what is said within a policy fixed point.
Sure, but the interesting thing to me isn’t fixed points in the input/output map, it’s properties (i.e. attractors that are allowed to be large sets) that propagate from the answers seen by a human in response to their queries, into their output.
Even if there’s a fixed point, you have to further prove that this fixed point is consistent—that it’s actually the answer to some askable question. I feel like this is sort of analogous to Hofstadter’s q-sequence.
In the giant lookup table space, HCH must converge to a cycle, although that convergence can be really slow. I think you have convergence to a stationary distribution if each layer is trained on a random mix of several previous layers. Of course, you can still have occilations in what is said within a policy fixed point.