diff --git a/reasoning_gym/graphs/path_star.py b/reasoning_gym/graphs/path_star.py
index 6b613b7c..c666dc85 100644
--- a/reasoning_gym/graphs/path_star.py
+++ b/reasoning_gym/graphs/path_star.py
@@ -30,6 +30,8 @@ Solve the following task:
 
 @dataclass
 class PathStarConfig:
+    """Configuration for Path Star dataset generation"""
+
     degree: int = 3
     node_range: int = 100_000
     min_path_length: int = 3
@@ -41,8 +43,13 @@ class PathStarConfig:
     seed: Optional[int] = None
 
     def validate(self) -> None:
-        assert self.degree >= 2 and self.min_path_length >= 1
-        assert self.node_range > self.degree * self.max_path_length + 1
+        """Validate configuration parameters"""
+        assert self.degree >= 2, "degree must be at least 2"
+        assert self.min_path_length >= 1, "min_path_length must be at least 1"
+        assert self.min_path_length <= self.max_path_length, "min_path_length must be <= max_path_length"
+        assert (
+            self.node_range > self.degree * self.max_path_length + 1
+        ), "node_range must exceed degree * max_path_length + 1 for unique labels"
 
 
 class PathStarDataset(ProceduralDataset):
@@ -51,6 +58,17 @@ class PathStarDataset(ProceduralDataset):
     def __init__(self, config: PathStarConfig):
         super().__init__(config=config, seed=config.seed, size=config.size)
 
+    def score_answer(self, answer: Optional[str], entry: dict[str, Any]) -> float:
+        """Score an answer. Path is unique in a star graph, so only exact match counts."""
+        if not isinstance(answer, str) or len(answer.strip()) == 0:
+            return 0.0
+        # Normalize: strip, collapse whitespace
+        answer_normalized = " ".join(answer.strip().split())
+        oracle_normalized = " ".join(entry["answer"].strip().split())
+        if answer_normalized == oracle_normalized:
+            return 1.0
+        return 0.0
+
     def __getitem__(self, idx: int) -> dict[str, Any]:
         rng = random.Random(self.seed + idx)
 
@@ -81,7 +99,10 @@ class PathStarDataset(ProceduralDataset):
         rng.shuffle(edges)
 
         edges_str = "".join(f"|{u} {v}" for u, v in edges)
-        prefix = f"{edges_str}/{center} {goal} = "
+        if cfg.reversed:
+            prefix = f"{edges_str}/{goal} {center} = "
+        else:
+            prefix = f"{edges_str}/{center} {goal} = "
         question = PROMPT_TEMPLATE.format(task=prefix)
 
         # gold path
@@ -94,6 +115,8 @@ class PathStarDataset(ProceduralDataset):
             "question": question,
             "answer": answer,
             "metadata": {
+                "source_dataset": DATASET_NAME,
+                "source_index": idx,
                 "center": center,
                 "goal": goal,
                 "path_length": path_length,
diff --git a/tests/test_path_star.py b/tests/test_path_star.py
new file mode 100644
index 00000000..2c1b9564
--- /dev/null
+++ b/tests/test_path_star.py
@@ -0,0 +1,195 @@
+"""Tests for Path Star graph problem generation"""
+
+import pytest
+
+from reasoning_gym.graphs.path_star import PathStarConfig, PathStarCurriculum, PathStarDataset
+
+
+def test_path_star_config_validation():
+    """Test that invalid configs raise appropriate errors"""
+    with pytest.raises(AssertionError):
+        config = PathStarConfig(degree=1)  # Must be >= 2
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = PathStarConfig(min_path_length=0)  # Must be >= 1
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = PathStarConfig(min_path_length=5, max_path_length=3)  # min > max
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = PathStarConfig(degree=3, max_path_length=5, node_range=16)  # node_range too small (need > 3*5+1=16)
+        config.validate()
+
+
+def test_path_star_dataset_deterministic():
+    """Test that dataset generates same items with same seed"""
+    config = PathStarConfig(seed=42, size=10)
+    dataset1 = PathStarDataset(config)
+    dataset2 = PathStarDataset(config)
+
+    for i in range(len(dataset1)):
+        assert dataset1[i] == dataset2[i]
+
+
+def test_path_star_dataset_items():
+    """Test basic properties of generated items"""
+    config = PathStarConfig(min_path_length=3, max_path_length=5, size=10, seed=42)
+    dataset = PathStarDataset(config)
+
+    for i in range(len(dataset)):
+        item = dataset[i]
+        # Check item structure
+        assert isinstance(item, dict)
+        assert "question" in item
+        assert "answer" in item
+        assert "metadata" in item
+
+        # Check metadata fields
+        assert item["metadata"]["source_dataset"] == "path_star"
+        assert item["metadata"]["source_index"] == i
+        assert "center" in item["metadata"]
+        assert "goal" in item["metadata"]
+        assert "path_length" in item["metadata"]
+        assert "goal_path" in item["metadata"]
+        assert "difficulty" in item["metadata"]
+
+        # Verify answer format: space-separated integers
+        answer_parts = item["answer"].split()
+        assert all(part.isdigit() for part in answer_parts)
+
+        # First node should be center, last should be goal
+        center = item["metadata"]["center"]
+        goal = item["metadata"]["goal"]
+        assert int(answer_parts[0]) == center
+        assert int(answer_parts[-1]) == goal
+
+        # Path length should match: center + path_length nodes
+        path_length = item["metadata"]["path_length"]
+        assert len(answer_parts) == path_length + 1
+
+        # Path length within configured range
+        assert config.min_path_length <= path_length <= config.max_path_length
+
+
+def test_path_star_dataset_iteration():
+    """Test that iteration respects dataset size"""
+    config = PathStarConfig(size=5, seed=42)
+    dataset = PathStarDataset(config)
+
+    items = list(dataset)
+    assert len(items) == config.size
+
+    # Test multiple iterations yield same items
+    assert items == list(dataset)
+
+
+def test_path_star_answer_correctness():
+    """Test that generated paths are valid by checking edge connectivity"""
+    config = PathStarConfig(size=20, seed=123)
+    dataset = PathStarDataset(config)
+
+    for i in range(len(dataset)):
+        item = dataset[i]
+        question = item["question"]
+        answer_parts = [int(x) for x in item["answer"].split()]
+
+        # Parse edges from the question
+        # Format: ...edges_str/start goal = ...
+        # Extract the task part between "Solve the following task:\n" and the end
+        task_line = question.split("Solve the following task:\n")[1].strip()
+        edge_part, _ = task_line.split("/")
+        edges = set()
+        for edge_str in edge_part.split("|"):
+            edge_str = edge_str.strip()
+            if edge_str:
+                u, v = edge_str.split()
+                edges.add((int(u), int(v)))
+
+        # Verify consecutive nodes in the answer are connected by edges
+        for j in range(len(answer_parts) - 1):
+            u, v = answer_parts[j], answer_parts[j + 1]
+            assert (u, v) in edges, f"Edge ({u}, {v}) not found in edges for item {i}"
+
+
+def test_path_star_score_answer():
+    """Test the score_answer method"""
+    config = PathStarConfig(seed=42, size=5)
+    dataset = PathStarDataset(config)
+    item = dataset[0]
+    oracle = item["answer"]
+
+    # Exact match
+    assert dataset.score_answer(oracle, item) == 1.0
+
+    # Match with extra whitespace
+    assert dataset.score_answer(f"  {oracle}  ", item) == 1.0
+
+    # Match with extra internal whitespace
+    spaced = oracle.replace(" ", "  ")
+    assert dataset.score_answer(spaced, item) == 1.0
+
+    # Wrong answer
+    assert dataset.score_answer("0 1 2 3", item) == 0.0
+
+    # None
+    assert dataset.score_answer(None, item) == 0.0
+
+    # Empty string
+    assert dataset.score_answer("", item) == 0.0
+
+
+def test_path_star_reversed():
+    """Test that reversed=True produces correct answer and task format"""
+    config_fwd = PathStarConfig(seed=42, size=5, reversed=False)
+    config_rev = PathStarConfig(seed=42, size=5, reversed=True)
+    dataset_fwd = PathStarDataset(config_fwd)
+    dataset_rev = PathStarDataset(config_rev)
+
+    for i in range(len(dataset_fwd)):
+        item_fwd = dataset_fwd[i]
+        item_rev = dataset_rev[i]
+
+        # Reversed answer should be the forward answer reversed
+        fwd_parts = item_fwd["answer"].split()
+        rev_parts = item_rev["answer"].split()
+        assert rev_parts == list(reversed(fwd_parts))
+
+        # Task format should swap start/goal
+        center = item_fwd["metadata"]["center"]
+        goal = item_fwd["metadata"]["goal"]
+        assert f"/{center} {goal} = " in item_fwd["question"]
+        assert f"/{goal} {center} = " in item_rev["question"]
+
+
+def test_path_star_curriculum():
+    """Test curriculum creates valid configs at various levels"""
+    curriculum = PathStarCurriculum()
+
+    base_value = {"size": 150, "seed": 1}
+
+    # Level 0 (base)
+    base_cfg: PathStarConfig = curriculum.generate_configuration(base_value)
+    assert base_cfg.seed == 1
+    assert base_cfg.size == 150
+    assert base_cfg.degree == 2
+    assert base_cfg.node_range == 10_000
+    assert base_cfg.min_path_length == 3 and base_cfg.max_path_length == 3
+
+    # Increment attributes
+    curriculum.increment_attr_level("degree")
+    curriculum.increment_attr_level("node_range")
+    curriculum.increment_attr_level("path_length")
+    increased_cfg = curriculum.generate_configuration(base_value)
+    assert increased_cfg.degree == 3
+    assert increased_cfg.node_range == 50_000
+    assert increased_cfg.min_path_length == 3 and increased_cfg.max_path_length == 5
+
+    # Decrement degree back
+    curriculum.decrement_attr_level("degree")
+    partial_cfg = curriculum.generate_configuration(base_value)
+    assert partial_cfg.degree == 2
+    assert partial_cfg.node_range == 50_000
+    assert partial_cfg.min_path_length == 3 and partial_cfg.max_path_length == 5