meta-sine-foundry

Meta-Sine Foundry

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.

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.

Reproduce

uv run python projects/meta-sine-foundry/train.py