diff --git a/extension/training/module/test/training_module_test.cpp b/extension/training/module/test/training_module_test.cpp
index 16ff87bc022..29d9bcf5842 100644
--- a/extension/training/module/test/training_module_test.cpp
+++ b/extension/training/module/test/training_module_test.cpp
@@ -199,3 +199,58 @@ TEST_F(TrainingModuleTest, DataExternalConstantsTest) {
   ASSERT_EQ(attributes.find("b")->second.sizes()[0], 2);
   ASSERT_EQ(attributes.find("b")->second.dim(), 2);
 }
+
+TEST_F(TrainingModuleTest, UnloadMethodTest) {
+  const char* ptd_path = std::getenv("ET_MODULE_TRAIN_DATA_PATH");
+  Result<FileDataLoader> data_map_loader_res = FileDataLoader::from(ptd_path);
+  ASSERT_EQ(data_map_loader_res.error(), Error::Ok);
+
+  auto data_map_loader =
+      std::make_unique<torch::executor::util::FileDataLoader>(
+          std::move(data_map_loader_res.get()));
+
+  const char* pte_path = std::getenv("ET_MODULE_TRAIN_PROGRAM_PATH");
+  Result<FileDataLoader> pte_loader_res = FileDataLoader::from(pte_path);
+  ASSERT_EQ(pte_loader_res.error(), Error::Ok);
+
+  auto pte_loader = std::make_unique<torch::executor::util::FileDataLoader>(
+      std::move(pte_loader_res.get()));
+
+  auto mod = executorch::extension::training::TrainingModule(
+      std::move(pte_loader),
+      nullptr,
+      nullptr,
+      nullptr,
+      std::move(data_map_loader));
+
+  auto parameters_res = mod.named_parameters("forward");
+  ASSERT_EQ(parameters_res.error(), Error::Ok);
+  auto& parameters = parameters_res.get();
+
+  ASSERT_NEAR(
+      parameters_res.get()
+          .find("linear.bias")
+          ->second.const_data_ptr<float>()[0],
+      0.1528,
+      0.0001);
+
+  // mock training
+  auto linear_bias_ptr =
+      parameters.find("linear.bias")->second.mutable_data_ptr<float>();
+  linear_bias_ptr[0] += 0.5;
+  ASSERT_NEAR(
+      parameters.find("linear.bias")->second.const_data_ptr<float>()[0],
+      0.6528,
+      0.0001);
+
+  mod.unload_method("forward");
+
+  auto new_parameters_res = mod.named_parameters("forward");
+  ASSERT_EQ(new_parameters_res.error(), Error::Ok);
+  ASSERT_NEAR(
+      new_parameters_res.get()
+          .find("linear.bias")
+          ->second.const_data_ptr<float>()[0],
+      0.1528,
+      0.0001);
+}
diff --git a/extension/training/module/training_module.h b/extension/training/module/training_module.h
index 7dd380d2709..146eb61bcb7 100644
--- a/extension/training/module/training_module.h
+++ b/extension/training/module/training_module.h
@@ -49,6 +49,15 @@ class ET_EXPERIMENTAL TrainingModule final
   explicit TrainingModule(Module&&) = delete;
   TrainingModule& operator=(Module&&) = delete;
 
+  // Redefine to erase the tensors pointing to the released memory.
+  inline bool unload_method(const std::string& method_name) {
+    method_named_gradients_.erase(method_name);
+    method_named_parameters_.erase(method_name);
+    method_named_attributes_.erase(method_name);
+
+    return methods_.erase(method_name);
+  }
+
   /**
    * Execute a specific method with the given input and retrieve output. Only
    * valid if the specified method is a joint graph. Loads the program and