diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
index 4960417abdb..bbdd4f8e1d4 100644
--- a/aten/src/ATen/native/native_functions.yaml
+++ b/aten/src/ATen/native/native_functions.yaml
@@ -4826,7 +4826,7 @@
   device_guard: False
   dispatch:
     CompositeImplicitAutograd: reshape_symint
-    CompositeImplicitAutogradNestedTensor: reshape_nested
+    CompositeImplicitAutogradNestedTensor: reshape_nested_symint
 
 - func: _reshape_copy(Tensor self, SymInt[] size) -> Tensor
   variants: function
diff --git a/aten/src/ATen/native/nested/NestedTensorMath.cpp b/aten/src/ATen/native/nested/NestedTensorMath.cpp
index c4bc824fdb3..442660ef8d7 100644
--- a/aten/src/ATen/native/nested/NestedTensorMath.cpp
+++ b/aten/src/ATen/native/nested/NestedTensorMath.cpp
@@ -894,7 +894,26 @@ Tensor reshape_nested(const Tensor& self, IntArrayRef proposed_shape) {
   }
 }
 
+Tensor reshape_nested_symint(const Tensor& self, SymIntArrayRef proposed_shape) {
+  // Jagged layout NT decomp
+  if (self.layout() == at::kJagged) {
+    // TODO: Expand decomp to handle other viewable cases
+    bool viewable = self.is_contiguous();
+    return (
+        viewable ? self.view_symint(proposed_shape) :
+        self.clone(at::MemoryFormat::Contiguous).view_symint(proposed_shape)
+    );
+  }
+
+  return reshape_nested(self, C10_AS_INTARRAYREF_SLOW(proposed_shape));
+}
+
 Tensor reshape_as_nested(const Tensor& self, const Tensor& other) {
+  // Jagged layout NT decomp
+  if (self.layout() == at::kJagged) {
+    return self.reshape_symint(other.sym_sizes());
+  }
+
   auto other_ptr = get_nested_tensor_impl(other);
   // TODO: this is to reproduce other_ptr->opt_sizes_
   //       if an accessor is provided in the future, can replace this
diff --git a/aten/src/ATen/native/nested/NestedTensorMath.h b/aten/src/ATen/native/nested/NestedTensorMath.h
index 82699859909..068cc6b51ee 100644
--- a/aten/src/ATen/native/nested/NestedTensorMath.h
+++ b/aten/src/ATen/native/nested/NestedTensorMath.h
@@ -75,5 +75,7 @@ C10_ALWAYS_INLINE std::pair<int64_t, int64_t> _check_nested_layer_norm_inputs(
   return std::make_pair(M, N);
 }
 
+Tensor reshape_nested(const Tensor& self, IntArrayRef proposed_shape);
+
 } // namespace native
 } // namespace at
diff --git a/test/test_nestedtensor.py b/test/test_nestedtensor.py
index e9deadbb251..65cdc0791e6 100644
--- a/test/test_nestedtensor.py
+++ b/test/test_nestedtensor.py
@@ -3138,22 +3138,41 @@ class TestNestedTensorSubclass(TestCase):
         self.assertEqual(nt.shape[:2], view.shape[:2])
 
     @xfailIfTorchDynamo
-    def test_reshape_decomp(self, device):
+    @parametrize("requires_grad", [False, True])
+    def test_reshape_decomp(self, device, requires_grad):
         # contiguous NT should result in view
         nt = random_nt_from_dims(
-            [3, None, 10], device=device, dtype=torch.float32, layout=torch.jagged)
+            [3, None, 10],
+            device=device,
+            dtype=torch.float32,
+            layout=torch.jagged,
+            requires_grad=requires_grad
+        )
         view = nt.reshape(-1, -1, 5, 2)
         self.assertEqual(view.shape[:2], nt.shape[:2])
         self.assertTrue(view._is_view() and view._base is nt)
+        # make sure gradients flow back
+        if requires_grad:
+            view.backward(torch.ones_like(view))
+            self.assertEqual(nt.grad, torch.ones_like(nt))
 
         # non-contiguous NT should result in contiguous copy
         nt = random_nt_from_dims(
-            [3, None, 5, 2], device=device, dtype=torch.float32, layout=torch.jagged)
+            [3, None, 5, 2],
+            device=device,
+            dtype=torch.float32,
+            layout=torch.jagged,
+            requires_grad=requires_grad
+        )
         nt_noncontig = nt.transpose(-1, -2)
         self.assertFalse(nt_noncontig.is_contiguous())
         copy = nt_noncontig.reshape(-1, -1, 10)
         self.assertTrue(copy.is_contiguous())
         self.assertEqual(copy.shape[:2], nt.shape[:2])
+        # make sure gradients flow back
+        if requires_grad:
+            copy.backward(torch.ones_like(copy))
+            self.assertEqual(nt.grad, torch.ones_like(nt))
 
     def test_flatten_decomp(self, device):
         nt = random_nt_from_dims(
diff --git a/torch/nested/_internal/ops.py b/torch/nested/_internal/ops.py
index b91812a3c2d..3aacd3eb2f4 100644
--- a/torch/nested/_internal/ops.py
+++ b/torch/nested/_internal/ops.py
@@ -280,20 +280,6 @@ def jagged_torch_function(func, *args, **kwargs):
     if func is torch._C._nn.scaled_dot_product_attention:
         return jagged_scaled_dot_product_attention(*args, **kwargs)
 
-    # Handle reshape() / reshape_as() here because they're CompositeImplicit.
-    # TODO: Do the full view determination logic based on computeStride()
-    if func.__name__ == "reshape":
-        inp = args[0]
-        shape = args[1:]
-
-        return inp.view(shape) if inp.is_contiguous() else inp.contiguous().view(shape)
-
-    if func.__name__ == "reshape_as":
-        inp = args[0]
-        other = args[1]
-
-        return inp.reshape(*other.shape)
-
     # Handle flatten() here because it's CompositeImplicit.
     if func.__name__ == "flatten":