diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
index 149d3abff5..a4deab8041 100644
--- a/examples/flax/image-captioning/run_image_captioning_flax.py
+++ b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -875,15 +875,19 @@ def main():
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBart.
-    # For FlaxT5, one should correct the layer norm parameter naming
-    # accordingly - see `run_t5_mlm_flax.py` e.g.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        layer_norm_params = [
-            (name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
-        ]
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer
diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py
index ee40d80cab..00fc6e61f7 100755
--- a/examples/flax/language-modeling/run_clm_flax.py
+++ b/examples/flax/language-modeling/run_clm_flax.py
@@ -638,15 +638,19 @@ def main():
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxGPT2.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {
-            path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
-            for path in flat_params
-        }
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer
diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
index 0343d02341..9657471246 100755
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -658,12 +658,19 @@ def main():
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer
diff --git a/examples/flax/language-modeling/run_t5_mlm_flax.py b/examples/flax/language-modeling/run_t5_mlm_flax.py
index c9ab71c2b9..ad0b43d3d6 100755
--- a/examples/flax/language-modeling/run_t5_mlm_flax.py
+++ b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -326,7 +326,6 @@ class FlaxDataCollatorForT5MLM:
     decoder_start_token_id: int
 
     def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
-
         # convert list to dict and tensorize input
         batch = BatchEncoding(
             {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
@@ -395,7 +394,6 @@ class FlaxDataCollatorForT5MLM:
         return input_ids
 
     def random_spans_noise_mask(self, length):
-
         """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .
 
         Noise mask consisting of random spans of noise tokens.
@@ -782,10 +780,17 @@ def main():
     # The mask is True for parameters that should be decayed.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {
-            path: (path[-1] != "bias" and path[-2:] not in [("layer_norm", "scale"), ("final_layer_norm", "scale")])
-            for path in flat_params
-        }
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer
diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index 740ad93966..b424756355 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -327,12 +327,19 @@ def create_train_state(
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     tx = optax.adamw(
diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
index 089b3c1911..a1b5fc37e2 100644
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -723,15 +723,19 @@ def main():
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBart.
-    # For FlaxT5, one should correct the layer norm parameter naming
-    # accordingly - see `run_t5_mlm_flax.py` e.g.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        layer_norm_params = [
-            (name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
-        ]
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer
diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
index 48ce733a23..d777123185 100755
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -226,7 +226,17 @@ def create_train_state(
     # The mask is True for parameters that should be decayed.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     tx = optax.adamw(
diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
index 6b3a20ae27..682fc03b8b 100644
--- a/examples/flax/token-classification/run_flax_ner.py
+++ b/examples/flax/token-classification/run_flax_ner.py
@@ -284,12 +284,19 @@ def create_train_state(
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        # find out all LayerNorm parameters
+        layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+        layer_norm_named_params = set(
+            [
+                layer[-2:]
+                for layer_norm_name in layer_norm_candidates
+                for layer in flat_params.keys()
+                if layer_norm_name in "".join(layer).lower()
+            ]
+        )
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     tx = optax.adamw(