Meta-Sine Foundry learns an initialization that can specialize to a new sinusoid
from five observations and a handful of gradient steps. Tasks vary in amplitude and
phase. A first-order MAML learner, an ordinary model trained on pooled tasks, and an
untrained architecture-matched control receive the same adaptation rule at test time.
Evaluation covers 200 seeded tasks and reports mean query MSE before adaptation,
after one support-set update, and after five updates. The benchmark tests rapid
adaptation, not whether the meta-learner has discovered a universal regression prior.
Verified results
Each architecture has 1,761 parameters. Evaluation used 200 unseen tasks, five
support points per task, and the same 0.01 inner learning rate.
Initialization
0 updates MSE
1 update MSE
5 updates MSE
First-order MAML
3.1180
1.8105
0.7417
Pooled pretraining
3.1389
3.6318
3.7822
Random initialization
4.4218
4.4110
4.6232
After five updates, the meta-learned initialization reduced mean query error by
80.39% versus pooled pretraining and 83.96% versus random initialization. Pooled and
random controls worsened under the meta-learned step size, which is part of the
measured adaptation advantage rather than a claim that they could not be retuned.
