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README.md
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# Tiny memorization experiment results
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Per-run learning curves (`loss_curve.csv`: step, epoch, lr, train_loss_bits, held_loss_bits),
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final metrics (`metrics.json` including memorized bits, bits/parameter, L_train, L_held),
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and raw `state.pt` checkpoints for every run. Aggregated `summary.csv`/`summary.json`
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and figures (`capacity_plot.png`, `loss_curves.png`) are at the repo root.
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Memorization metric (paper Sec 3.2): `mem = N_data_tokens * (log2 V - L_train_bits)`;
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`bits_per_param = mem / n_params`. Capacity ~ max memorization over dataset sizes.
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