Add degradation experiments: one-parameter-at-a-time ablations

Controlled study of how the model degrades. Four variants, each = v1 with a
single field replaced (dataclasses.replace), so differences are attributable:
- small: capacity down (2L/64d, ~0.38M params)
- short: training time down (max_iters 20k->2k)
- ctx:   context window down (256->64)
- data:  data quantity down (max_train_tokens->1M; new TrainConfig knob + train.py slice)

- scripts/compare.py: sample same prompt across all trained configs with a
  shared seed, print side by side, write reports/compare.md
- tests/test_configs.py: enforces one-parameter-at-a-time (only intended fields
  differ from v1) + small param count (3 tests). Full suite: 29 passing.

configs/data.py

@@ -0,0 +1,17 @@
+"""Degradation experiment: same as v1, but trained on a tiny slice of data.
+
+Isolates *data quantity*. Only max_train_tokens changes from v1: the model sees
+~1M training tokens instead of 555M. Expect overfitting — train loss falls while
+val loss (measured on the full held-out set) climbs, and novel prompts collapse
+as the model regurgitates memorized stories.
+
+Tip: this overfits within a few thousand steps. Watch the val loss turn upward
+in the log and stop early (Ctrl-C) rather than running all 20,000 steps.
+"""
+
+from dataclasses import replace
+
+import v1
+
+model = v1.model
+train = replace(v1.train, max_train_tokens=1_000_000)