diff --git a/minerva/models/nets/__init__.py b/minerva/models/nets/__init__.py
index c6c35d5..bbfdb31 100644
--- a/minerva/models/nets/__init__.py
+++ b/minerva/models/nets/__init__.py
@@ -4,7 +4,6 @@
 from .image.unet import UNet
 from .image.wisenet import WiseNet
 from .mlp import MLP
-from .lfr import LearnFromRandomnessModel, RepeatedModuleList
 
 __all__ = [
     "SimpleSupervisedModel",
@@ -12,7 +11,5 @@
     "SETR_PUP",
     "UNet",
     "WiseNet",
-    "MLP",
-    "LearnFromRandomnessModel",
-    "RepeatedModuleList"
+    "MLP"
 ]
diff --git a/minerva/models/nets/mlp.py b/minerva/models/nets/mlp.py
index 8452749..b59e2c0 100644
--- a/minerva/models/nets/mlp.py
+++ b/minerva/models/nets/mlp.py
@@ -1,12 +1,14 @@
 from torch import nn
+from typing import Sequence
+
 
 class MLP(nn.Sequential):
     """
     A multilayer perceptron (MLP) implemented as a subclass of nn.Sequential.
-    
-    The MLP consists of a series of linear layers interleaved with ReLU activation functions,
-    except for the last layer which is purely linear.
-    
+
+    This MLP is composed of a sequence of linear layers interleaved with ReLU activation
+    functions, except for the final layer which remains purely linear.
+
     Example
     -------
 
@@ -21,32 +23,51 @@ class MLP(nn.Sequential):
     )
     """
 
-    def __init__(self, *layer_sizes: int):
+    def __init__(
+        self,
+        layer_sizes: Sequence[int],
+        activation_cls: type = nn.ReLU,
+        *args,
+        **kwargs
+    ):
         """
-        Initializes the MLP with the given layer sizes.
+        Initializes the MLP with specified layer sizes.
 
         Parameters
         ----------
-        *layer_sizes: int
-            A variable number of positive integers specifying the size of each layer.
-            There must be at least two integers, representing the input and output layers.
-        
+        layer_sizes : Sequence[int]
+            A sequence of positive integers indicating the size of each layer.
+            At least two integers are required, representing the input and output layers.
+        activation_cls : type
+            The class of the activation function to use between layers. Default is nn.ReLU.
+        *args
+            Additional arguments passed to the activation function.
+        **kwargs
+            Additional keyword arguments passed to the activation function.
+
         Raises
         ------
-        AssertionError: If less than two layer sizes are provided.
-        
-        AssertionError: If any layer size is not a positive integer.
+        AssertionError
+            If fewer than two layer sizes are provided or if any layer size is not a positive integer.
+        AssertionError
+            If activation_cls does not inherit from torch.nn.Module.
         """
+
         assert (
             len(layer_sizes) >= 2
         ), "Multilayer perceptron must have at least 2 layers"
         assert all(
             ls > 0 and isinstance(ls, int) for ls in layer_sizes
-        ), "All layer sizes must be a positive integer"
+        ), "All layer sizes must be positive integers"
+
+        assert issubclass(
+            activation_cls, nn.Module
+        ), "activation_cls must inherit from torch.nn.Module"
 
         layers = []
         for i in range(len(layer_sizes) - 2):
-            layers += [nn.Linear(layer_sizes[i], layer_sizes[i + 1]), nn.ReLU()]
-        layers += [nn.Linear(layer_sizes[-2], layer_sizes[-1])]
+            layers.append(nn.Linear(layer_sizes[i], layer_sizes[i + 1]))
+            layers.append(activation_cls(*args, **kwargs))
+        layers.append(nn.Linear(layer_sizes[-2], layer_sizes[-1]))
 
         super().__init__(*layers)
diff --git a/minerva/models/ssl/__init__.py b/minerva/models/ssl/__init__.py
new file mode 100644
index 0000000..c14ad54
--- /dev/null
+++ b/minerva/models/ssl/__init__.py
@@ -0,0 +1,6 @@
+from .lfr import LearnFromRandomnessModel, RepeatedModuleList
+
+__all__ = [
+    "LearnFromRandomnessModel",
+    "RepeatedModuleList"
+]
\ No newline at end of file
diff --git a/minerva/models/nets/lfr.py b/minerva/models/ssl/lfr.py
similarity index 100%
rename from minerva/models/nets/lfr.py
rename to minerva/models/ssl/lfr.py
diff --git a/tests/models/nets/test_lfr.py b/tests/models/ssl/test_lfr.py
similarity index 95%
rename from tests/models/nets/test_lfr.py
rename to tests/models/ssl/test_lfr.py
index 666e050..c93cfec 100644
--- a/tests/models/nets/test_lfr.py
+++ b/tests/models/ssl/test_lfr.py
@@ -2,7 +2,7 @@
 from torch.nn import Sequential, Conv2d, CrossEntropyLoss
 from torchvision.transforms import Resize
 
-from minerva.models.nets.lfr import RepeatedModuleList, LearnFromRandomnessModel
+from minerva.models.ssl.lfr import RepeatedModuleList, LearnFromRandomnessModel
 from minerva.models.nets.image.deeplabv3 import DeepLabV3Backbone
 
 
@@ -57,4 +57,3 @@ def __init__(self):
     # Test the configure_optimizers method
     optimizer = model.configure_optimizers()
     assert optimizer is not None
-