fix: stratified Trip sampling (prefix was all City=3)

scripts/job_trip.sh

@@ -9,7 +9,7 @@ snapshot_download('ashishk1331/ccd-repro-code', repo_type='dataset', local_dir='
 cd /work && mkdir -p outputs
 nvidia-smi --query-gpu=name,memory.total --format=csv
 
-N_TRIP=${N_TRIP:-60}
+N_TRIP=${N_TRIP:-64}
 N_ABL=${N_ABL:-40}
 
 python - <<'EOF'

scripts/run_eval.py

@@ -34,13 +34,38 @@ TASK_DEFAULTS = {
 # ---------------------------------------------------------------- data / prompts
 
 def load_trip(limit=None, num_cities=None, seed=0):
+    """Load Trip Plan examples.
+
+    IMPORTANT: the benchmark file is ordered by difficulty -- the first 200 of the
+    1600 examples all have num_cities=3 (the easiest tier), then 200 with 4, etc.
+    Taking a prefix therefore samples ONLY the easiest tier and inflates the score
+    (measured: 55% on a prefix-60 vs the paper's 15.10% over the full set).
+    When no explicit tier is requested we take a *stratified* sample: equal numbers
+    from each num_cities tier, so the subset matches the full benchmark's mix.
+    """
     with open(os.path.join(DATA, "trip_planning.json")) as f:
         data = json.load(f)
     items = list(data.values())
     if num_cities is not None:
+        # single-tier request (e.g. the Claim 5 City=3 ablation): prefix is fine,
+        # every example in the tier is equivalent for sampling purposes
         items = [i for i in items if i["num_cities"] == str(num_cities)]
-    if limit:
-        items = items[:limit]   # deterministic prefix; the file order is fixed
+        if limit:
+            items = items[:limit]
+    elif limit:
+        import random
+        tiers = sorted({i["num_cities"] for i in items}, key=int)
+        per = limit // len(tiers)
+        rng = random.Random(seed)
+        picked = []
+        for t in tiers:
+            pool = [i for i in items if i["num_cities"] == t]
+            picked.extend(rng.sample(pool, min(per, len(pool))))
+        # top up any remainder deterministically from the unpicked pool
+        if len(picked) < limit:
+            rest = [i for i in items if i not in picked]
+            picked.extend(rng.sample(rest, limit - len(picked)))
+        items = picked
     # 2-shot prompt, exactly as DreamLM/Dream eval_planning.py::eval_trip
     prompts = []
     for i in items: