Phase 4: dependency-free ASCII loss-curve plotter

- scripts/plot_loss.py: reads reports/loss-<config>.csv, renders train/val
  loss as a terminal ASCII chart, saves it to reports/loss-<config>.txt.
  Verified on a synthetic curve. Keeps deps at torch + numpy.
- README: documented plotting choice and the Phase 4 run commands.

README.md

@@ -109,9 +109,19 @@ raw text ──► tokenizer training ──► tokenized corpus ──► pretr
 - Runs on MPS; expected wall-clock for v1 is a few hours for ~100–200M tokens
   (roughly Chinchilla-optimal for this size — we don't need to see the whole
   corpus).
-- Loss curve logged to a plain CSV and plotted locally.
-- Deliverables: `src/train.py`, `configs/v1.yaml` (or `.py`), checkpoints in
-  `checkpoints/` (gitignored), loss curves in `reports/`.
+- Loss curve logged to a plain CSV (`reports/loss-v1.csv`) and rendered as an
+  ASCII chart by `scripts/plot_loss.py` — no plotting dependency; the chart is
+  also saved to `reports/loss-v1.txt` as a committable artifact.
+- Deliverables: `src/train.py`, `configs/v1.py`, `scripts/plot_loss.py`,
+  checkpoints in `checkpoints/` (gitignored), loss log + chart in `reports/`.
+
+Commands:
+
+```sh
+python src/train.py --config v1 --overfit   # sanity check (loss -> ~0)
+python src/train.py --config v1             # real run (resumable: --resume)
+python scripts/plot_loss.py --config v1     # ASCII loss curve
+```
 
 ### Phase 5 — Sampling & evaluation
 - Autoregressive sampler with temperature and top-k.