feat(sleep): 3-way train/val/test split + gate_mode on|off

Data-split refactor (the anti-overfitting foundation the user asked for):
  - TaskRecord gains split∈{train,val,test} and origin∈{real,dream}.
  - assign_splits: real tasks deterministically split into val/test (disjoint);
    DREAM-augmented tasks (origin='dream') NEVER enter val/test — they only go to
    train. val gates updates; test is the final held-out measure.
  - gbrain loader maps its held-out.jsonl -> test, benchmark.jsonl -> train/val,
    so the gbrain held-out stays the true final score.
  - consolidate(): train drives reflect, val gates; adds gate_mode='off' (greedy,
    no hard filter) reporting val movement (greedy_improved/regressed/flat).
  - run_gbrain/transfer/experiment score on test (val fallback); run_gbrain gains
    --gate on|off. Legacy replay/holdout names normalized.

New test proves dream tasks never land in val/test. 21 tests pass; mock
experiment + gate=off both green.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

skillopt/sleep/config.py

@@ -30,10 +30,13 @@ DEFAULTS: Dict[str, Any] = {
     # ── budgets ────────────────────────────────────────────────────────────
     "max_tasks_per_night": 40,
     "max_tokens_per_night": 400_000,
-    "holdout_fraction": 0.34,     # fraction of mined tasks reserved for the gate
+    "holdout_fraction": 0.34,     # legacy alias for val_fraction
+    "val_fraction": 0.34,         # real tasks reserved to gate updates
+    "test_fraction": 0.0,         # real tasks reserved as the final held-out measure
     # ── optimizer ──────────────────────────────────────────────────────────
     "backend": "mock",            # "mock" | "claude" | "codex"
     "model": "",                  # backend-specific; "" => backend default
+    "gate_mode": "on",            # "on" (validation-gated) | "off" (greedy, no hard filter)
     "codex_path": "",             # "" => auto-detect the real @openai/codex binary
     "edit_budget": 4,             # textual learning rate (max edits/night)
     "gate_metric": "mixed",       # hard | soft | mixed (mixed best for tiny holdouts)