# Tiny memorization experiment results

Per-run learning curves (`loss_curve.csv`: step, epoch, lr, train_loss_bits, held_loss_bits),
final metrics (`metrics.json` including memorized bits, bits/parameter, L_train, L_held),
and raw `state.pt` checkpoints for every run. Aggregated `summary.csv`/`summary.json`
and figures (`capacity_plot.png`, `loss_curves.png`) are at the repo root.

Memorization metric (paper Sec 3.2): `mem = N_data_tokens * (log2 V - L_train_bits)`;
`bits_per_param = mem / n_params`. Capacity ~ max memorization over dataset sizes.