diff --git a/orttraining/orttraining/core/graph/gradient_builder.cc b/orttraining/orttraining/core/graph/gradient_builder.cc
index 5576e8ccb1..ee9707f77c 100755
--- a/orttraining/orttraining/core/graph/gradient_builder.cc
+++ b/orttraining/orttraining/core/graph/gradient_builder.cc
@@ -1994,5 +1994,14 @@ IMPLEMENT_GRADIENT_BUILDER(GetLSTMGradient) {
   return {NodeDef(OpDef{"LSTMGrad", kMSDomain, 1}, input_args, output_args, SrcNodeAttributes())};
 }
 
+IMPLEMENT_GRADIENT_BUILDER(GetReciprocalGradient) {
+  // y = 1 / x
+  // dy/dx = -1 / x^2
+  // dL/dx = dL/dy * dy/dx = dL/dy * (-1 / x^2)
+  return {NodeDef("Mul", {O(0), O(0)}, {IA("Square_O0")}),
+          NodeDef("Neg", {IA("Square_O0")}, {IA("Neg_Square_O0")}),
+          NodeDef("Mul", {GO(0), IA("Neg_Square_O0")}, {GI(0)})};
+}
+
 }  // namespace training
 }  // namespace onnxruntime
diff --git a/orttraining/orttraining/core/graph/gradient_builder.h b/orttraining/orttraining/core/graph/gradient_builder.h
index 30da6e53ba..f3b5be06f5 100755
--- a/orttraining/orttraining/core/graph/gradient_builder.h
+++ b/orttraining/orttraining/core/graph/gradient_builder.h
@@ -85,6 +85,7 @@ DECLARE_GRADIENT_BUILDER(GetScatterElementsGradient)
 DECLARE_GRADIENT_BUILDER(GetTriluGradient)
 DECLARE_GRADIENT_BUILDER(GetFakeQuantGradient)
 DECLARE_GRADIENT_BUILDER(GetLSTMGradient)
+DECLARE_GRADIENT_BUILDER(GetReciprocalGradient)
 
 DECLARE_GRADIENT_BUILDER(GetExternalGradient)
 
diff --git a/orttraining/orttraining/core/graph/gradient_builder_registry.cc b/orttraining/orttraining/core/graph/gradient_builder_registry.cc
index ca0a397ced..bbb7f174e5 100755
--- a/orttraining/orttraining/core/graph/gradient_builder_registry.cc
+++ b/orttraining/orttraining/core/graph/gradient_builder_registry.cc
@@ -117,6 +117,7 @@ void GradientBuilderRegistry::RegisterGradientBuilders() {
   REGISTER_GRADIENT_BUILDER("Trilu", GetTriluGradient);
   REGISTER_GRADIENT_BUILDER("FakeQuant", GetFakeQuantGradient);
   REGISTER_GRADIENT_BUILDER("LSTMTraining", GetLSTMGradient);
+  REGISTER_GRADIENT_BUILDER("Reciprocal", GetReciprocalGradient);
 
   REGISTER_GRADIENT_BUILDER("ExternalGradient", GetExternalGradient);
 };
diff --git a/orttraining/orttraining/test/gradient/gradient_ops_test.cc b/orttraining/orttraining/test/gradient/gradient_ops_test.cc
index b6f2639512..4834451616 100644
--- a/orttraining/orttraining/test/gradient/gradient_ops_test.cc
+++ b/orttraining/orttraining/test/gradient/gradient_ops_test.cc
@@ -3027,6 +3027,12 @@ TEST(GradientCheckerTest, PadAndUnflattenGrad) {
 }
 #endif
 
+TEST(GradientCheckerTest, ReciprocalGrad) {
+  // Avoid division by 0 by using the transformer.
+  std::function<float(float)> transformer = [](float x) { return x > 0 ? x + 0.2f : x - 0.2f; };
+  UnaryOpGradientTest("Reciprocal", kOnnxDomain, 12, nullptr, &transformer);
+}
+
 }  // namespace test
 }  // namespace onnxruntime
 
diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
index 335b3f051a..e0dbc44741 100644
--- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
+++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
@@ -6140,3 +6140,34 @@ def test_cache_exported_model():
         torch.onnx.export.reset_mock()
 
         del os.environ["ORTMODULE_CACHE_DIR"]
+
+
+def test_reciprocal_gradient():
+    class ReciprocalModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x):
+            return 1 / x
+
+    def run_step(model, x):
+        prediction = model(x)
+        loss = prediction.sum()
+        loss.backward()
+        return prediction, loss
+
+    device = "cuda"
+    pt_model = ReciprocalModel().to(device)
+    ort_model = ORTModule(copy.deepcopy(pt_model))
+
+    pt_x = torch.zeros(3, 224, 224, requires_grad=True, device=device)
+    with torch.no_grad():
+        pt_x[pt_x <= 0] -= 0.2
+        pt_x[pt_x > 0] += 0.2
+    ort_x = copy.deepcopy(pt_x)
+
+    pt_prediction, pt_loss = run_step(pt_model, pt_x)
+    ort_prediction, ort_loss = run_step(ort_model, ort_x)
+    _test_helpers.assert_values_are_close(pt_prediction, ort_prediction)
+    _test_helpers.assert_values_are_close(pt_loss, ort_loss)
+    _test_helpers.assert_values_are_close(pt_x.grad, ort_x.grad)