diff --git a/reasoning_gym/algorithmic/cryptarithm.py b/reasoning_gym/algorithmic/cryptarithm.py
index f3aa4c09..bc930848 100644
--- a/reasoning_gym/algorithmic/cryptarithm.py
+++ b/reasoning_gym/algorithmic/cryptarithm.py
@@ -262,7 +262,7 @@ class CryptarithmDataset(ProceduralDataset):
 
         # case 3 : partial score for the number of correct mapping answer
         total_correct, total = 0, 0
-        for alphabet, number in correct_mapping:
+        for alphabet, number in correct_mapping.items():
             total += 1
             if alphabet in predict_mapping:
                 if predict_mapping[alphabet] == number:
diff --git a/tests/test_cryptarithm.py b/tests/test_cryptarithm.py
index 0ae3ea7f..e0ae9c85 100644
--- a/tests/test_cryptarithm.py
+++ b/tests/test_cryptarithm.py
@@ -103,3 +103,73 @@ def test_max_letters_constraint():
 
         # Check total unique letters doesn't exceed 10 (digits 0-9)
         assert len(letter_to_digit) <= 10, "Total unique letters should not exceed 10"
+
+def test_cryptarithm_score_answer():
+    """Test the CryptarithmDataset.score_answer method for various correctness levels."""
+    dataset = create_dataset("cryptarithm", seed=42, size=1)
+    puzzle = dataset[0]
+    correct_answer_str = puzzle["answer"]  # e.g. "A=1,B=7,..."
+
+    # 1) Missing 'ANSWER:' => score should be 0.0
+    score = dataset.score_answer(answer=None, answer_str=correct_answer_str)
+    assert score == 0.0, f"Expected 0.0 when missing 'ANSWER:' prefix, got {score}"
+
+    # 2) Correct mapping => expecting 1.0
+    user_answer = f"ANSWER:\n{correct_answer_str}"
+    score = dataset.score_answer(answer=user_answer, answer_str=correct_answer_str)
+    assert score == 1.0, f"Expected 1.0 for perfectly correct answer, got {score}"
+
+    # 3) Mismatch number of pairs => score should be 0.1
+    # For instance, drop the last pair
+    splitted = correct_answer_str.split(',')
+    mismatch_str = ','.join(splitted[:-1])
+    user_answer = f"ANSWER:\n{mismatch_str}"
+    score = dataset.score_answer(answer=user_answer, answer_str=correct_answer_str)
+    assert score == 0.1, f"Expected 0.1 when #pairs does not match, got {score}"
+
+    # 4) Parse error => 0.15 (e.g. remove '=' from the first pair)
+    splitted = correct_answer_str.split(',')
+    splitted[0] = splitted[0].replace('=', '')  # remove '=' in the first pair
+    parse_error_str = ','.join(splitted)
+    user_answer = f"ANSWER:\n{parse_error_str}"
+    score = dataset.score_answer(answer=user_answer, answer_str=correct_answer_str)
+    assert score == 0.15, f"Expected 0.15 when parsing fails on at least one pair, got {score}"
+
+    # 5) Correct number of pairs, but duplicate alphabets => 0.3
+    # This makes the dictionary have fewer unique keys than expected
+    splitted = correct_answer_str.split(',')
+    if len(splitted) > 1:
+        splitted[0] = splitted[1]  # Duplicate the second pair in the first position
+    duplicates_str = ','.join(splitted)
+    user_answer = f"ANSWER:\n{duplicates_str}"
+    score = dataset.score_answer(answer=user_answer, answer_str=correct_answer_str)
+    assert score == 0.3, f"Expected 0.3 if the final dict has fewer unique alphabets, got {score}"
+
+    # 6) Partial correctness => some correct, some incorrect
+    splitted = correct_answer_str.split(',')
+    correct_mapping = {}
+    for pair in splitted:
+        alpha, num_str = pair.split('=')
+        correct_mapping[alpha] = int(num_str)
+
+    # Make exactly half of them correct, half incorrect
+    total = len(correct_mapping)
+    half = total // 2
+    new_pairs = []
+    i = 0
+    for alpha, num in correct_mapping.items():
+        if i < half:
+            new_pairs.append(f"{alpha}={num}")        # keep correct
+        else:
+            new_pairs.append(f"{alpha}={(num+1) % 10}")  # make incorrect
+        i += 1
+
+    partial_answer_str = ','.join(new_pairs)
+    user_answer = f"ANSWER:\n{partial_answer_str}"
+    score = dataset.score_answer(answer=user_answer, answer_str=correct_answer_str)
+
+    # The formula is (num_correct / total) * 0.7 + 0.3
+    expected_score = (half / total) * 0.7 + 0.3
+    assert abs(score - expected_score) < 1e-9, (
+        f"Partial correctness: expected {expected_score}, got {score}"
+    )
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