diff --git a/reasoning_gym/arithmetic/__init__.py b/reasoning_gym/arithmetic/__init__.py
index 46e5cfdf..8f4f9019 100644
--- a/reasoning_gym/arithmetic/__init__.py
+++ b/reasoning_gym/arithmetic/__init__.py
@@ -9,6 +9,7 @@ Arithmetic tasks for training reasoning capabilities:
 from .basic_arithmetic import ArithmeticDataset, ArithmeticDatasetConfig, arithmetic_dataset
 from .chain_sum import ChainSum, ChainSumConfig, chain_sum_dataset
 from .leg_counting import LegCountingConfig, LegCountingDataset, leg_counting_dataset
+from .prime_factorization import PrimeFactorizationConfig, PrimeFactorizationDataset, prime_factorization_dataset
 
 __all__ = [
     "ArithmeticDataset",
@@ -19,5 +20,8 @@ __all__ = [
     "chain_sum_dataset",
     "LegCountingConfig",
     "LegCountingDataset",
-    "leg_counting_dataset"
+    "leg_counting_dataset",
+    "PrimeFactorizationConfig",
+    "PrimeFactorizationDataset",
+    "prime_factorization_dataset"
 ]
diff --git a/reasoning_gym/arithmetic/prime_factorization.py b/reasoning_gym/arithmetic/prime_factorization.py
new file mode 100644
index 00000000..8359efac
--- /dev/null
+++ b/reasoning_gym/arithmetic/prime_factorization.py
@@ -0,0 +1,94 @@
+"""Prime factorization task generator"""
+from dataclasses import dataclass
+from random import Random
+from typing import List, Optional, Tuple
+
+@dataclass
+class PrimeFactorizationConfig:
+    """Configuration for prime factorization task generation"""
+    min_value: int = 2         # Minimum number to factorize
+    max_value: int = 1000      # Maximum number to factorize
+    seed: Optional[int] = None
+    size: int = 500           # Virtual dataset size
+
+    def validate(self):
+        """Validate configuration parameters"""
+        assert self.min_value >= 2, "min_value must be >= 2"
+        assert self.max_value >= self.min_value, "max_value must be >= min_value"
+
+
+class PrimeFactorizationDataset:
+    """Generates prime factorization tasks"""
+
+    def __init__(self, config: PrimeFactorizationConfig):
+        self.config = config
+        self.config.validate()
+        self.seed = config.seed if config.seed is not None else Random().randint(0, 2**32)
+
+    def __len__(self) -> int:
+        return self.config.size
+
+    def __iter__(self):
+        self._current_idx = 0
+        return self
+
+    def __next__(self):
+        if self._current_idx >= self.config.size:
+            raise StopIteration
+        item = self[self._current_idx]
+        self._current_idx += 1
+        return item
+
+    def _prime_factors(self, n: int) -> List[int]:
+        """Compute prime factors of a number"""
+        factors = []
+        d = 2
+        while n > 1:
+            while n % d == 0:
+                factors.append(d)
+                n //= d
+            d += 1
+            if d * d > n:
+                if n > 1:
+                    factors.append(n)
+                break
+        return factors
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single prime factorization task"""
+        rng = Random(self.seed + idx)
+        
+        # Generate random number to factorize
+        number = rng.randint(self.config.min_value, self.config.max_value)
+        
+        # Calculate prime factors
+        factors = self._prime_factors(number)
+        
+        # Format answer as multiplication of prime factors
+        answer = " × ".join(map(str, factors))
+        
+        return {
+            "question": (f"Find the prime factorization of {number}. "
+                       f"(Example: 12 = 2 × 2 × 3)"),
+            "answer": answer,
+            "metadata": {
+                "number": number,
+                "factors": factors
+            }
+        }
+
+
+def prime_factorization_dataset(
+    min_value: int = 2,
+    max_value: int = 1000,
+    seed: Optional[int] = None,
+    size: int = 500,
+) -> PrimeFactorizationDataset:
+    """Create a PrimeFactorizationDataset with the given configuration."""
+    config = PrimeFactorizationConfig(
+        min_value=min_value,
+        max_value=max_value,
+        seed=seed,
+        size=size,
+    )
+    return PrimeFactorizationDataset(config)