diff --git a/reasoning_gym/algorithmic/unscramble_words.py b/reasoning_gym/algorithmic/unscramble_words.py
index 87febd7f..0dd4521d 100644
--- a/reasoning_gym/algorithmic/unscramble_words.py
+++ b/reasoning_gym/algorithmic/unscramble_words.py
@@ -19,6 +19,7 @@ class UnscrambleWordsConfig:
     max_words: int = 20  # Maximum words per task
     min_corruption_level: float = 0.1  # Minimum fraction of characters to swap
     max_corruption_level: float = 0.9  # Maximum fraction of characters to swap
+    consecutive_words: bool = True  # Whether to select consecutive words from text
     seed: Optional[int] = None
     size: int = 500  # Virtual dataset size
 
@@ -54,7 +55,7 @@ class UnscrambleWordsDataset(ProceduralDataset):
             return word
             
         word = list(word)
-        num_swaps = int(len(word) * corruption_level)
+        num_swaps = max(1, int(len(word) * corruption_level))  # Ensure at least one swap
         
         for _ in range(num_swaps):
             # Pick two different random positions
@@ -72,8 +73,14 @@ class UnscrambleWordsDataset(ProceduralDataset):
         num_words = rng.randint(self.config.min_words, self.config.max_words)
         corruption_level = rng.uniform(self.config.min_corruption_level, self.config.max_corruption_level)
         
-        # Select random words
-        selected_words = rng.sample(self.words, num_words)
+        # Select words based on configuration
+        if self.config.consecutive_words:
+            # Select consecutive words from a random starting position
+            start_idx = rng.randint(0, len(self.words) - num_words)
+            selected_words = self.words[start_idx:start_idx + num_words]
+        else:
+            # Select random words
+            selected_words = rng.sample(self.words, num_words)
         
         # Scramble each word
         scrambled_words = [