diff --git a/metagpt/actions/write_plan.py b/metagpt/actions/write_plan.py
index f7ca1ff4c..11a3f3e1e 100644
--- a/metagpt/actions/write_plan.py
+++ b/metagpt/actions/write_plan.py
@@ -13,6 +13,7 @@ from metagpt.actions import Action
 from metagpt.prompts.ml_engineer import ASSIGN_TASK_TYPE_PROMPT, ASSIGN_TASK_TYPE
 from metagpt.schema import Message, Task, Plan
 from metagpt.utils.common import CodeParser, create_func_config
+from metagpt.logs import logger
 
 
 class WritePlan(Action):
@@ -22,6 +23,7 @@ class WritePlan(Action):
     # Task:
     Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to __max_tasks__ tasks.
     If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.
+    If you encounter errors on the current task, revise and output the current single task only.
     Output a list of jsons following the format:
     ```json
     [
@@ -76,7 +78,13 @@ def rsp_to_tasks(rsp: str) -> List[Task]:
 
 def update_plan_from_rsp(rsp: str, current_plan: Plan):
     tasks = rsp_to_tasks(rsp)
-    if len(tasks) == 1:
+    if len(tasks) == 1 or tasks[0].dependent_task_ids:
+        if tasks[0].dependent_task_ids and len(tasks) > 1:
+            # tasks[0].dependent_task_ids means the generated tasks are not a complete plan
+            # for they depend on tasks in the current plan, in this case, we only support updating one task each time
+            logger.warning(
+                "Current plan will take only the first generated task if the generated tasks are not a complete plan"
+            )
         # handle a single task
         if current_plan.has_task_id(tasks[0].task_id):
             # replace an existing task
diff --git a/metagpt/roles/ml_engineer.py b/metagpt/roles/ml_engineer.py
index fe6f81841..de649e857 100644
--- a/metagpt/roles/ml_engineer.py
+++ b/metagpt/roles/ml_engineer.py
@@ -28,7 +28,7 @@ class MLEngineer(Role):
 
         self.plan = Plan(goal=goal)
         self.use_tools = False
-        self.use_code_steps = True
+        self.use_code_steps = False
         self.execute_code = ExecutePyCode()
         self.auto_run = auto_run
 
@@ -64,6 +64,11 @@ class MLEngineer(Role):
             # ask for acceptance, users can other refuse and change tasks in the plan
             review, task_result_confirmed = await self._ask_review(trigger=ReviewConst.TASK_REVIEW_TRIGGER)
 
+            if self.auto_run:
+                # if human confirms the task result, then we deem the task completed, regardless of whether the code run succeeds;
+                # if auto mode, then the code run has to succeed for the task to be considered completed
+                task_result_confirmed = success
+
             if task_result_confirmed:
                 # tick off this task and record progress
                 task.code = code
@@ -143,7 +148,7 @@ class MLEngineer(Role):
             if not success and counter >= max_retry:
                 logger.info("coding failed!")
                 review, _ = await self._ask_review(auto_run=False, trigger=ReviewConst.CODE_REVIEW_TRIGGER)
-                if ReviewConst.CHANGE_WORD in review:
+                if ReviewConst.CHANGE_WORD[0] in review:
                     counter = 0  # redo the task again with help of human suggestions
 
         return code, result, success, code_steps
@@ -199,9 +204,12 @@ class MLEngineer(Role):
         # TODO dataset description , code steps
         user_requirement = self.plan.goal
         data_desc = self.plan.context
-        tasks = json.dumps(
-            [task.dict() for task in self.plan.tasks], indent=4, ensure_ascii=False
-        )
+        tasks = [task.dict() for task in self.plan.tasks]
+        for task in tasks:
+            # Shorten the context as we don't need code steps after we get the codes.
+            # This doesn't affect current_task below, which should hold the code steps
+            task.pop("code_steps")
+        tasks = json.dumps(tasks, indent=4, ensure_ascii=False)
         current_task = self.plan.current_task.json() if self.plan.current_task else {}
         context = STRUCTURAL_CONTEXT.format(
             user_requirement=user_requirement, data_desc=data_desc, tasks=tasks, current_task=current_task
@@ -219,7 +227,8 @@ if __name__ == "__main__":
     # requirement = "Run data analysis on sklearn Diabetes dataset, include a plot"
     # requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy"
     # requirement = "Run data analysis on sklearn Wisconsin Breast Cancer dataset, include a plot, train a model to predict targets (20% as validation), and show validation accuracy"
-    requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
+    # requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
+    requirement = "This is a house price dataset, your goal is to predict the sale price of a property based on its features. The target column is SalePrice. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report RMSE between the logarithm of the predicted value and the logarithm of the observed sales price on the eval data. Train data path: 'workspace/house-prices-advanced-regression-techniques/split_train.csv', eval data path: 'workspace/house-prices-advanced-regression-techniques/split_eval.csv'."
 
     async def main(requirement: str = requirement, auto_run: bool = False):
         role = MLEngineer(goal=requirement, auto_run=auto_run)