diff --git a/src/relay/transforms/first_order_gradient.cc b/src/relay/transforms/first_order_gradient.cc
index 55714592ded7..3419cb670a28 100644
--- a/src/relay/transforms/first_order_gradient.cc
+++ b/src/relay/transforms/first_order_gradient.cc
@@ -174,11 +174,14 @@ struct FirstOrderReverseAD : ExprFunctor<ADValue(const Expr&)> {
   }
 
   ADValue VisitExpr_(const TupleGetItemNode* op) final {
-    Expr e = GetRef<Expr>(op);
     ADValue tup = VisitExpr(op->tuple);
-    auto tt = op->tuple->checked_type().as<TupleTypeNode>();
+    TupleType tt = Downcast<TupleType>(op->tuple->checked_type());
     size_t idx = op->index;
-    auto ret = std::make_shared<ADTensor>(ll, e, diag_ctx);
+    // reconstruct projection using let-bound variable to avoid duplicating input tuple
+    TupleGetItem orig = TupleGetItem(tup->get<ADTensor>().forward, idx);
+    orig->checked_type_ = op->checked_type();
+    auto ret = std::make_shared<ADTensor>(ll, orig, diag_ctx);
+    // for orig = pi(tup, i), pi_grad(tup, i, g) = G where pi(G, i) = g and pi(G, j) = 0 for j != i
     backprop_actions.push_back([tup, tt, idx, ret](LetList* ll) {
       auto& ad_tup = tup->get<ADTensor>();
       std::vector<Expr> updated_grads;
@@ -193,16 +196,26 @@ struct FirstOrderReverseAD : ExprFunctor<ADValue(const Expr&)> {
   }
 
   ADValue VisitExpr_(const TupleNode* op) final {
-    Expr e = GetRef<Expr>(op);
-    std::vector<ADValue> fields;
+    auto tt = Downcast<TupleType>(op->checked_type());
+    std::vector<ADValue> ad_fields;
+    std::vector<Expr> field_bindings;
     for (const auto& f : op->fields) {
-      fields.push_back(VisitExpr(f));
+      ADValue f_ad = VisitExpr(f);
+      if (!dynamic_cast<ADTensor*>(f_ad.get())) {
+        diag_ctx.EmitFatal(Diagnostic::Error(f->span)
+                           << "first-order AD only supports (nested) tuples of tensors");
+      }
+      ad_fields.push_back(f_ad);
+      field_bindings.push_back(f_ad->get<ADTensor>().forward);
     }
-    auto tt = op->checked_type().as<TupleTypeNode>();
-    auto ret = std::make_shared<ADTensor>(ll, e, diag_ctx);
-    backprop_actions.push_back([fields, tt, ret](LetList* ll) {
-      for (size_t i = 0; i < fields.size(); ++i) {
-        auto& ad_field = fields[i]->get<ADTensor>();
+    // reconstruct tuple using let-bound variables to avoid duplication
+    auto orig = Tuple(field_bindings);
+    orig->checked_type_ = tt;
+    auto ret = std::make_shared<ADTensor>(ll, orig, diag_ctx);
+    // for orig = tuple(x1, ..., xn), tuple_grad(x1, ..., xn, G) = [pi(G, 1), ..., pi(G, n)]
+    backprop_actions.push_back([ad_fields, tt, ret](LetList* ll) {
+      for (size_t i = 0; i < ad_fields.size(); ++i) {
+        auto& ad_field = ad_fields[i]->get<ADTensor>();
         ad_field.reverse =
             LiftedAdd(tt->fields[i], ad_field.reverse, GetField(ret->reverse, i), ll);
       }
diff --git a/tests/python/relay/test_pass_gradient.py b/tests/python/relay/test_pass_gradient.py
index 6228c5fc157b..cd0edf95aba7 100644
--- a/tests/python/relay/test_pass_gradient.py
+++ b/tests/python/relay/test_pass_gradient.py
@@ -429,6 +429,23 @@ def test_no_duplication():
     assert counts["nn.dense"] == 3, "We expect 3 dense (1 forward, two backward)"
 
 
+def test_no_duplication_tuples():
+    x = tvm.relay.Var("x", type_annotation=tvm.relay.TensorType([12, 12]))
+    y = tvm.relay.Var("y", type_annotation=tvm.relay.TensorType([12, 12]))
+    xy = tvm.relay.nn.dense(x, y)
+
+    t = relay.Tuple([xy, xy])
+
+    m = tvm.relay.sum(xy, keepdims=True)
+    s = tvm.relay.sum(relay.TupleGetItem(t, 0) - m)
+    fn = tvm.relay.Function([x, y], s)
+    fn = run_infer_type(fn)
+    gr = tvm.relay.transform.gradient(fn, mode="first_order")
+
+    counts = count_ops(gr)
+    assert counts["nn.dense"] == 3, "We expect 3 dense (1 forward, two backward)"
+
+
 def test_global_function():
     m = tvm.IRModule()
     shape = (10, 10)