diff --git a/torchrl/modules/models/models.py b/torchrl/modules/models/models.py
index 49c0b0961ab..cad4065f54a 100644
--- a/torchrl/modules/models/models.py
+++ b/torchrl/modules/models/models.py
@@ -955,7 +955,7 @@ class DuelingCnnDQNet(nn.Module):
             >>> cnn_kwargs = {
             ...     'num_cells': [32, 64, 64],
             ...     'strides': [4, 2, 1],
-            ...     'kernels': [8, 4, 3],
+            ...     'kernel_sizes': [8, 4, 3],
             ... }
 
         mlp_kwargs (dict or list of dicts, optional): kwargs for the advantage
diff --git a/tutorials/sphinx-tutorials/getting-started-3.py b/tutorials/sphinx-tutorials/getting-started-3.py
index 594cb7392c0..70ffe37a005 100644
--- a/tutorials/sphinx-tutorials/getting-started-3.py
+++ b/tutorials/sphinx-tutorials/getting-started-3.py
@@ -152,7 +152,7 @@
 indices = buffer.extend(data)
 
 #################################
-# We can check that the buffer now has the same number of elements than what
+# We can check that the buffer now has the same number of elements as what
 # we got from the collector:
 
 assert len(buffer) == collector.frames_per_batch
@@ -174,7 +174,7 @@
 # Next steps
 # ----------
 #
-# - You can have look at other multirpocessed
+# - You can have look at other multiprocessed
 #   collectors such as :class:`~torchrl.collectors.collectors.MultiSyncDataCollector` or
 #   :class:`~torchrl.collectors.collectors.MultiaSyncDataCollector`.
 # - TorchRL also offers distributed collectors if you have multiple nodes to
diff --git a/tutorials/sphinx-tutorials/rb_tutorial.py b/tutorials/sphinx-tutorials/rb_tutorial.py
index 4f5ecb4936d..2a852f0e364 100644
--- a/tutorials/sphinx-tutorials/rb_tutorial.py
+++ b/tutorials/sphinx-tutorials/rb_tutorial.py
@@ -168,7 +168,7 @@
 buffer_lazytensor = ReplayBuffer(storage=LazyTensorStorage(size))
 
 ######################################################################
-# Let us create a batch of data of size ``torch.Size([3])` with 2 tensors
+# Let us create a batch of data of size ``torch.Size([3])`` with 2 tensors
 # stored in it:
 #