diff --git a/reasoning_gym/dataset.py b/reasoning_gym/dataset.py
index 6837e40f..bbfe3895 100644
--- a/reasoning_gym/dataset.py
+++ b/reasoning_gym/dataset.py
@@ -9,8 +9,9 @@ from typing import Any, Dict, Iterator, Optional
 class ProceduralDataset(ABC, Sized, Iterable[Dict[str, Any]]):
     """Abstract base class for procedural dataset generators"""
 
-    def __init__(self, seed: Optional[int] = None, size: int = 500):
-        """Initialize the dataset with optional seed and size"""
+    def __init__(self, config: Any, seed: Optional[int] = None, size: int = 500):
+        """Initialize the dataset with config, optional seed and size"""
+        self.config = config
         self.size = size
         self.seed = seed if seed is not None else Random().randint(0, 2**32)
 
diff --git a/reasoning_gym/factory.py b/reasoning_gym/factory.py
new file mode 100644
index 00000000..b482d1b1
--- /dev/null
+++ b/reasoning_gym/factory.py
@@ -0,0 +1,75 @@
+from dataclasses import is_dataclass
+from typing import Any, Dict, Type, TypeVar
+
+from .dataset import ProceduralDataset
+
+# Type variables for generic type hints
+ConfigT = TypeVar('ConfigT')
+DatasetT = TypeVar('DatasetT', bound=ProceduralDataset)
+
+# Global registry of datasets
+_DATASETS: Dict[str, tuple[Type[ProceduralDataset], Type]] = {}
+
+def register_dataset(
+    name: str,
+    dataset_cls: Type[DatasetT],
+    config_cls: Type[ConfigT]
+) -> None:
+    """
+    Register a dataset class with its configuration class.
+    
+    Args:
+        name: Unique identifier for the dataset
+        dataset_cls: Class derived from ProceduralDataset
+        config_cls: Configuration dataclass for the dataset
+    
+    Raises:
+        ValueError: If name is already registered or invalid types provided
+    """
+    if name in _DATASETS:
+        raise ValueError(f"Dataset '{name}' is already registered")
+    
+    if not issubclass(dataset_cls, ProceduralDataset):
+        raise ValueError(
+            f"Dataset class must inherit from ProceduralDataset, got {dataset_cls}"
+        )
+    
+    if not is_dataclass(config_cls):
+        raise ValueError(
+            f"Config class must be a dataclass, got {config_cls}"
+        )
+    
+    _DATASETS[name] = (dataset_cls, config_cls)
+
+def create_dataset(
+    name: str,
+    config: Any,
+    seed: int = None,
+    size: int = 500
+) -> ProceduralDataset:
+    """
+    Create a dataset instance by name with the given configuration.
+    
+    Args:
+        name: Registered dataset name
+        config: Configuration instance for the dataset
+        seed: Optional random seed
+        size: Size of the dataset (default: 500)
+    
+    Returns:
+        Configured dataset instance
+        
+    Raises:
+        ValueError: If dataset not found or config type mismatch
+    """
+    if name not in _DATASETS:
+        raise ValueError(f"Dataset '{name}' not found")
+        
+    dataset_cls, config_cls = _DATASETS[name]
+    
+    if not isinstance(config, config_cls):
+        raise ValueError(
+            f"Config must be instance of {config_cls.__name__}, got {type(config).__name__}"
+        )
+        
+    return dataset_cls(config=config, seed=seed, size=size)