Transplanting algorithms into randomly initialized networks. I wonder if you could train a policy network to walk upright in sim, back out the “walk upright” algorithm, randomly initialize a new network which can call that algorithm as a “subroutine call” (but the walk-upright weights are frozen), and then have the new second model learn to call that subroutine appropriately? Possibly the learned representations would be convergently similar enough to interface quickly via SGD update dynamics.
This is basically how I view the DeepMind Flamingo model training to have operated, where a few stitching layers learn to translate the outputs of a frozen vision encoder into “subroutine calls” into the frozen language model, such that visual concept circuits ping their corresponding text token output circuits.
Transplanting algorithms into randomly initialized networks. I wonder if you could train a policy network to walk upright in sim, back out the “walk upright” algorithm, randomly initialize a new network which can call that algorithm as a “subroutine call” (but the walk-upright weights are frozen), and then have the new second model learn to call that subroutine appropriately? Possibly the learned representations would be convergently similar enough to interface quickly via SGD update dynamics.
If so, this provides some (small, IMO) amount of rescue for the “evolved modularity” hypothesis in light of information inaccessibility.
This is basically how I view the DeepMind Flamingo model training to have operated, where a few stitching layers learn to translate the outputs of a frozen vision encoder into “subroutine calls” into the frozen language model, such that visual concept circuits ping their corresponding text token output circuits.