diff --git a/keras/src/layers/normalization/rms_normalization.py b/keras/src/layers/normalization/rms_normalization.py
index f97ee6c3685d..f769c3c5359b 100644
--- a/keras/src/layers/normalization/rms_normalization.py
+++ b/keras/src/layers/normalization/rms_normalization.py
@@ -13,12 +13,10 @@ class RMSNormalization(Layer):
     [Root Mean Square Layer Normalization](https://arxiv.org/pdf/1910.07467)
     by Biao Zhang et al.
 
+    The layer scales the normalized outputs via a learnable scaling factor
+    (`scale`).
 
-    If `scale` is enabled, the layer will scale the normalized outputs via
-    a learnable scaling factor.
-
-    So, with scaling enabled, the normalization equations
-    are as follows:
+    The normalization equations are as follows:
 
     Let the intermediate activations for a mini-batch to be the `inputs`.
 
@@ -39,7 +37,18 @@ class RMSNormalization(Layer):
 
     Args:
         axis: int. The axis on which to perform the normalization.
+            Typically, this is the features axis. `-1` is the last dimension
+            in the input. Defaults to `-1`.
         epsilon: float. A small number to add to avoid division by zero.
+            Defaults to `1e-6`.
+
+    Input shape:
+        Arbitrary. Use the keyword argument `input_shape` (tuple of integers,
+        does not include the samples axis) when using this layer as the first
+        layer in a model.
+
+    Output shape:
+        Same shape as input.
     """
 
     def __init__(self, axis=-1, epsilon=1e-6, **kwargs):
@@ -62,15 +71,6 @@ def build(self, input_shape):
         self.built = True
 
     def call(self, x):
-        """Applies RMS normalization to the input tensor.
-
-        Args:
-            x: Input tensor of shape (batch_size, input_dim).
-
-        Returns:
-            The RMS-normalized tensor of the same shape (batch_size, input_dim),
-            scaled by the learned `scale` parameter.
-        """
         return ops.rms_normalization(
             x, scale=self.scale, axis=self.axis, epsilon=self.epsilon
         )