diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index c3116f9988ce..462d1cf92c01 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -91,7 +91,7 @@ def get_constant(
     # Convert if possible
     if isinstance(var, relax.Var) and var.name_hint in params:
         # When converting a parameter to a constant, update references to it as well.
-        _, value = params.pop(var.name_hint)
+        _, value = params[var.name_hint]
         const_value = relax.const(value)
         graph_nodes[var.name_hint] = const_value
         return const_value
@@ -2152,7 +2152,7 @@ def _parse_graph_initializers(self, graph: onnx.onnx_ml_pb2.GraphProto):
                 init_var = self._new_var(var_name, shape=array.shape, dtype=array.dtype)
                 self._nodes[init_tensor.name] = init_var
                 # We need to keep track of both the real value and variable for this variable.
-                self._params[init_tensor.name] = (init_var, array)
+                self._params[var_name] = (init_var, array)
             # Otherwise we can use the weight as a constant.
             else:
                 self._nodes[init_tensor.name] = relax.const(array)
diff --git a/tests/python/relax/test_frontend_onnx.py b/tests/python/relax/test_frontend_onnx.py
index 3ea987973578..8f4e9881f497 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -1909,5 +1909,48 @@ def test_multi_inputs_with_same_symbolic_shape():
     check_correctness(model)
 
 
+def test_multi_ops_with_same_params():
+    reshape_node_1 = helper.make_node("Reshape", ["a", "x"], ["b"])
+    reshape_node_2 = helper.make_node("Reshape", ["b", "x"], ["c"])
+
+    a_shape = [16]
+    output_shape = [1, 16]
+
+    graph = helper.make_graph(
+        [reshape_node_1, reshape_node_2],
+        "test_multi_ops_with_same_params",
+        inputs=[
+            helper.make_tensor_value_info("a", TensorProto.FLOAT, a_shape),
+        ],
+        initializer=[
+            helper.make_tensor("x", TensorProto.INT64, [2], output_shape),
+        ],
+        outputs=[helper.make_tensor_value_info("c", TensorProto.FLOAT, output_shape)],
+    )
+    model = helper.make_model(graph, producer_name="test_multi_ops_with_same_params")
+    check_correctness(model)
+
+
+def test_params_names_start_with_onnx():
+    reshape_node = helper.make_node("Reshape", ["a", "onnx::x"], ["b"])
+
+    a_shape = [16]
+    output_shape = [1, 16]
+
+    graph = helper.make_graph(
+        [reshape_node],
+        "test_params_names_start_with_onnx",
+        inputs=[
+            helper.make_tensor_value_info("a", TensorProto.FLOAT, a_shape),
+        ],
+        initializer=[
+            helper.make_tensor("onnx::x", TensorProto.INT64, [2], output_shape),
+        ],
+        outputs=[helper.make_tensor_value_info("b", TensorProto.FLOAT, output_shape)],
+    )
+    model = helper.make_model(graph, producer_name="test_params_names_start_with_onnx")
+    check_correctness(model)
+
+
 if __name__ == "__main__":
     tvm.testing.main()