diff --git a/docs/scens/kaggle_agent.rst b/docs/scens/kaggle_agent.rst
index 3b39dc5b..8500de0e 100644
--- a/docs/scens/kaggle_agent.rst
+++ b/docs/scens/kaggle_agent.rst
@@ -240,5 +240,3 @@ The following environment variables can be set in the `.env` file to customize t
     :members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
     :exclude-members: Config, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler, v2_add_fail_attempt_to_latest_successful_execution, new_knowledge_base_path, knowledge_base_path, data_folder, data_folder_debug, select_threshold
     :no-index:
-
-
diff --git a/rdagent/components/coder/model_coder/model_execute_template_v2.txt b/rdagent/components/coder/model_coder/model_execute_template_v2.txt
index 41201d99..f0af8d0b 100644
--- a/rdagent/components/coder/model_coder/model_execute_template_v2.txt
+++ b/rdagent/components/coder/model_coder/model_execute_template_v2.txt
@@ -12,7 +12,9 @@ valid_X = pd.DataFrame(np.random.randn(8, 30), columns=[f"{i}" for i in range(30
 valid_y = pd.Series(np.random.randint(0, 2, 8))
 
 model = fit(train_X, train_y, valid_X, valid_y)
-execution_model_output = predict(model, valid_X).cpu().detach().numpy()
+execution_model_output = predict(model, valid_X)
+if isinstance(execution_model_output, torch.Tensor):
+    execution_model_output = execution_model_output.cpu().detach().numpy()
 
 execution_feedback_str = f"Execution successful, output numpy ndarray shape: {execution_model_output.shape}"
 
diff --git a/rdagent/scenarios/kaggle/developer/runner.py b/rdagent/scenarios/kaggle/developer/runner.py
index 1e02bba5..a1868f6a 100644
--- a/rdagent/scenarios/kaggle/developer/runner.py
+++ b/rdagent/scenarios/kaggle/developer/runner.py
@@ -25,6 +25,8 @@ class KGCachedRunner(CachedRunner[ASpecificExp]):
         for f in sorted((exp.experiment_workspace.workspace_path / "model").glob("*.py"), key=lambda x: x.name):
             codes.append(f.read_text())
         codes = "\n".join(codes)
+        for i in range(len(exp.sub_workspace_list)):
+            codes += str(exp.sub_workspace_list[i].code_dict.values())
         return md5_hash(codes)
 
     @cache_with_pickle(get_cache_key, CachedRunner.assign_cached_result)
@@ -58,7 +60,6 @@ class KGModelRunner(KGCachedRunner[KGModelExperiment]):
             else:
                 model_file_name = f"model/model_{model_type.lower()}.py"
                 exp.experiment_workspace.inject_code(**{model_file_name: sub_ws.code_dict["model.py"]})
-
         env_to_use = {"PYTHONPATH": "./"}
 
         result = exp.experiment_workspace.execute(run_env=env_to_use)