# Additive Analogies in Parameter Space

Meta-learning, which exposes a network to many related functions so it can later learn similar ones quickly, leaves the adapted parameter vectors of that network in an additive geometric relationship. On the ABgeom sequence-generation task, randomly initialized transformers reached a mean F-score of 0.062 after five training batches, while meta-learned initializations reached 1.00 after one step; parameter vectors recombined by symbolic analogy produced networks that implemented the intended functions without direct training, at a mean F-score of 1.00 over 100 analogies. The effect appeared across transformers and LSTMs and three function domains, but the analogies hold only around a single initialization.

Meta-learning, which exposes a network to many related functions so it can later learn similar ones quickly, leaves the adapted parameter vectors of that network in an additive geometric relationship. On the ABgeom sequence-generation task, randomly initialized transformers reached a mean F-score of 0.062 after five training batches, while meta-learned initializations reached 1.00 after one step; parameter vectors recombined by symbolic analogy produced networks that implemented the intended functions without direct training, at a mean F-score of 1.00 over 100 analogies. The effect appeared across transformers and LSTMs and three function domains, but the analogies hold only around a single initialization.