diff --git a/python/tvm/relax/frontend/torch/exported_program_translator.py b/python/tvm/relax/frontend/torch/exported_program_translator.py
index 97ccc6393cbb..50c29397d7ee 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -390,9 +390,11 @@ def create_convert_map(
             "reshape.default": self._reshape,
             # tensor creation
             "_to_copy.default": self._to_copy,
+            "arange.default": self._arange,
+            "arange.start": self._arange,
+            "arange.start_step": self._arange,
             "detach.default": self._detach,
             "detach_.default": self._detach,
-            "arange.start": self._arange,
             "contiguous.default": lambda node: self.env[node.args[0]],  # no-op
             "clone.default": lambda node: self.env[node.args[0]],
             "empty.memory_format": self._empty,
diff --git a/tests/python/relax/test_from_exported_to_cuda.py b/tests/python/relax/test_from_exported_to_cuda.py
index 19b8f80a2390..56ee527caf09 100644
--- a/tests/python/relax/test_from_exported_to_cuda.py
+++ b/tests/python/relax/test_from_exported_to_cuda.py
@@ -467,6 +467,39 @@ def forward(self, x):
     assert_torch_output_vs_tvm_from_exported_to_cuda(raw_data, torch_module, target, dev)
 
 
+@tvm.testing.parametrize_targets("cuda")
+def test_arange(target, dev):
+    # arange.default
+    raw_data = np.array([0, 0, 0, 0, 0])
+
+    class ArangeDefaultModel(nn.Module):
+        def forward(self, x):
+            return x + torch.arange(5)
+
+    torch_module = ArangeDefaultModel().eval()
+    assert_torch_output_vs_tvm_from_exported_to_cuda(raw_data, torch_module, target, dev)
+
+    # arange.start
+    raw_data = np.array([0, 0, 0])
+
+    class ArangeStartModel(nn.Module):
+        def forward(self, x):
+            return x + torch.arange(1, 4)
+
+    torch_module = ArangeStartModel().eval()
+    assert_torch_output_vs_tvm_from_exported_to_cuda(raw_data, torch_module, target, dev)
+
+    # arange.start_step
+    raw_data = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+
+    class ArangeStartStopModel(nn.Module):
+        def forward(self, x):
+            return x + torch.arange(1, 2.5, 0.5, dtype=torch.float32)
+
+    torch_module = ArangeStartStopModel().eval()
+    assert_torch_output_vs_tvm_from_exported_to_cuda(raw_data, torch_module, target, dev)
+
+
 @tvm.testing.parametrize_targets("cuda")
 def test_index_select(target, dev):
     class IndexSelectModel(nn.Module):