diff --git a/src/transformers/models/switch_transformers/modeling_switch_transformers.py b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
index 935152b4ff49..689e15535eb2 100644
--- a/src/transformers/models/switch_transformers/modeling_switch_transformers.py
+++ b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
@@ -102,11 +102,19 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         # https://huggingface.co/papers/2101.03961.
         # We also store the previous dtype to cast back the output to the previous dtype
         self.input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(self.dtype)
+
+        # Create a copy for applying jitter noise
+        routing_states = hidden_states.clone()
+        routing_states = routing_states.to(self.dtype)
+
         if self.training and self.jitter_noise > 0:
-            # Multiply the token inputs by the uniform distribution - adding some noise
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
-        router_logits = self.classifier(hidden_states)
+            # Apply jitter noise only to the routing copy
+            routing_states *= torch.empty_like(routing_states).uniform_(
+                1.0 - self.jitter_noise, 1.0 + self.jitter_noise
+            )
+
+        # Use jittered states for routing decisions
+        router_logits = self.classifier(routing_states)
 
         # Apply Softmax and cast back to the original `dtype`
         router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
@@ -623,7 +631,7 @@ def _init_weights(self, module):
             module.weight.data.fill_(factor * 1.0)
         elif isinstance(
             module,
-            (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
+            SwitchTransformersModel | SwitchTransformersForConditionalGeneration | SwitchTransformersEncoderModel,
         ):
             module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
diff --git a/src/transformers/models/switch_transformers/modular_switch_transformers.py b/src/transformers/models/switch_transformers/modular_switch_transformers.py
index cf4eaf0cedff..ec18790f0940 100644
--- a/src/transformers/models/switch_transformers/modular_switch_transformers.py
+++ b/src/transformers/models/switch_transformers/modular_switch_transformers.py
@@ -159,11 +159,19 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         # https://huggingface.co/papers/2101.03961.
         # We also store the previous dtype to cast back the output to the previous dtype
         self.input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(self.dtype)
+
+        # Create a copy for applying jitter noise
+        routing_states = hidden_states.clone()
+        routing_states = routing_states.to(self.dtype)
+
         if self.training and self.jitter_noise > 0:
-            # Multiply the token inputs by the uniform distribution - adding some noise
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
-        router_logits = self.classifier(hidden_states)
+            # Apply jitter noise only to the routing copy
+            routing_states *= torch.empty_like(routing_states).uniform_(
+                1.0 - self.jitter_noise, 1.0 + self.jitter_noise
+            )
+
+        # Use jittered states for routing decisions
+        router_logits = self.classifier(routing_states)
 
         # Apply Softmax and cast back to the original `dtype`
         router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
@@ -352,7 +360,7 @@ def _init_weights(self, module):
             module.weight.data.fill_(factor * 1.0)
         elif isinstance(
             module,
-            (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
+            SwitchTransformersModel | SwitchTransformersForConditionalGeneration | SwitchTransformersEncoderModel,
         ):
             module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
diff --git a/tests/models/switch_transformers/test_modeling_switch_transformers.py b/tests/models/switch_transformers/test_modeling_switch_transformers.py
index 86238c053a35..2a3da6931911 100644
--- a/tests/models/switch_transformers/test_modeling_switch_transformers.py
+++ b/tests/models/switch_transformers/test_modeling_switch_transformers.py
@@ -1024,6 +1024,50 @@ def test_max_routing_capacity(self):
 
         assert torch.sum(expert_index) <= batch_size * self.config.num_experts * self.config.expert_capacity
 
+    def test_jitter_noise_preserves_hidden_states(self):
+        r"""
+        Test that jitter noise is applied only to routing decisions and does not modify the original hidden states.
+        This tests the fix for the jitter noise issue where noise was corrupting the input hidden states.
+        """
+        # Create a config with jitter noise enabled
+        config = SwitchTransformersConfig(
+            num_experts=2,
+            hidden_size=4,
+            d_ff=8,
+            router_jitter_noise=0.1,  # Enable jitter noise
+            expert_capacity=4,
+        )
+
+        # Create router
+        router = SwitchTransformersTop1Router(config)
+        router.eval()  # Set to eval mode first to test training mode separately
+
+        # Create input hidden states
+        hidden_states = torch.tensor([[[0.5, 0.2, 0.1, 0.3], [0.4, 0.6, 0.2, 0.8]]], dtype=torch.float32)
+
+        # Test in eval mode - no jitter noise should be applied
+        original_hidden_states = hidden_states.clone()
+        with torch.no_grad():
+            router_probs, expert_index, router_logits = router(hidden_states)
+
+        # Hidden states should remain unchanged in eval mode
+        self.assertTrue(torch.equal(hidden_states, original_hidden_states))
+
+        # Test in training mode - jitter noise should be applied only internally
+        router.train()
+        torch.manual_seed(42)  # Set seed for reproducible results
+
+        original_hidden_states = hidden_states.clone()
+        with torch.no_grad():
+            router_probs_train, expert_index_train, router_logits_train = router(hidden_states)
+
+        # Hidden states should still remain unchanged after router call
+        self.assertTrue(torch.equal(hidden_states, original_hidden_states))
+
+        # Results should be different between eval and train mode due to jitter noise
+        # (though this might occasionally fail due to randomness, it's very unlikely with seed)
+        self.assertFalse(torch.allclose(router_logits, router_logits_train, atol=1e-5))
+
 
 @slow
 @require_torch