diff --git a/examples/fabric/reinforcement_learning/train_fabric_decoupled.py b/examples/fabric/reinforcement_learning/train_fabric_decoupled.py
index 1d8967ec6f1dc..f3b0c74cb9064 100644
--- a/examples/fabric/reinforcement_learning/train_fabric_decoupled.py
+++ b/examples/fabric/reinforcement_learning/train_fabric_decoupled.py
@@ -274,7 +274,7 @@ def trainer(
         if group_rank == 0:
             metrics = {}
 
-        # Lerning rate annealing
+        # Learning rate annealing
         if args.anneal_lr:
             linear_annealing(optimizer, update, num_updates, args.learning_rate)
         if group_rank == 0:
diff --git a/tests/tests_fabric/loggers/test_csv.py b/tests/tests_fabric/loggers/test_csv.py
index f03e8c5125e54..08ed3990c2435 100644
--- a/tests/tests_fabric/loggers/test_csv.py
+++ b/tests/tests_fabric/loggers/test_csv.py
@@ -148,7 +148,7 @@ def test_automatic_step_tracking(tmp_path):
 
 
 @mock.patch(
-    # Mock the existance check, so we can simulate appending to the metrics file
+    # Mock the existence check, so we can simulate appending to the metrics file
     "lightning.fabric.loggers.csv_logs._ExperimentWriter._check_log_dir_exists"
 )
 def test_append_metrics_file(_, tmp_path):
@@ -189,7 +189,7 @@ def test_append_columns(tmp_path):
 
 
 @mock.patch(
-    # Mock the existance check, so we can simulate appending to the metrics file
+    # Mock the existence check, so we can simulate appending to the metrics file
     "lightning.fabric.loggers.csv_logs._ExperimentWriter._check_log_dir_exists"
 )
 def test_rewrite_with_new_header(_, tmp_path):
diff --git a/tests/tests_pytorch/accelerators/test_xla.py b/tests/tests_pytorch/accelerators/test_xla.py
index 6322a36a6cbc1..83dace719371d 100644
--- a/tests/tests_pytorch/accelerators/test_xla.py
+++ b/tests/tests_pytorch/accelerators/test_xla.py
@@ -148,7 +148,7 @@ def on_train_start(self):
 
     def on_train_end(self):
         # this might fail if run in an environment with too many ranks, as the total
-        # length of the dataloader will be distrbuted among them and then each rank might not do 3 steps
+        # length of the dataloader will be distributed among them and then each rank might not do 3 steps
         assert self.called["training_step"] == 3
         assert self.called["on_train_batch_start"] == 3
         assert self.called["on_train_batch_end"] == 3