diff --git a/examples/grpo_sciworld/sciworld.yaml b/examples/grpo_sciworld/sciworld.yaml
index 350b37ba80..43f7a13af3 100644
--- a/examples/grpo_sciworld/sciworld.yaml
+++ b/examples/grpo_sciworld/sciworld.yaml
@@ -19,7 +19,7 @@ buffer:
       storage_type: file
       path: 'scripts/data_prepare/sciworld_data'
       format:
-        prompt_key: 'game_file'
+        prompt_key: 'task_desc'
       rollout_args:
         repeat_times: 8
         temperature: 1.0
diff --git a/trinity/common/workflows/envs/webshop/webshop_workflow.py b/trinity/common/workflows/envs/webshop/webshop_workflow.py
index 6b961116d0..0003217e35 100644
--- a/trinity/common/workflows/envs/webshop/webshop_workflow.py
+++ b/trinity/common/workflows/envs/webshop/webshop_workflow.py
@@ -191,9 +191,31 @@ def __init__(
             model=model,
             task=task,
         )
+        self.max_env_steps = 15
+        self.reset(task)
+
+        # TODO: Make parallel envs
+        try:
+            import gym
+            from web_agent_site.envs import WebAgentTextEnv  # noqa: F401
+        except Exception as e:
+            print("Please make sure you have installed the web_agent_site package.")
+            error_message = f"Error importing WebAgentTextEnv {str(e)}. Please make sure you have installed the web_agent_site package, following the instructions in https://github.com/princeton-nlp/WebShop"
+            raise ImportError(error_message)
+        print("Making GYM env")
+        # NOTE: Hosting the env require ~15GB CPU memory.
+        # If you want easier env, you can set the num_products to 1000 or 100000.
+        self.env = gym.make(
+            "WebAgentTextEnv-v0", observation_mode="text_rich", num_products=None, human_goals=True
+        )
+
+    @property
+    def resettable(self):
+        return True
+
+    def reset(self, task: Task):
         self.task_desc = task.task_desc or "0"
         self.repeat_times = task.rollout_args.repeat_times
-        self.max_env_steps = 15
 
     def get_model_response(self, messages):
         responses = self.model.chat(messages, repeat_times=1)
@@ -242,26 +264,10 @@ def generate_env_inference_samples(self, env, session_id, rollout_num) -> List[E
                 {"session_id": session_id, "env_rounds": r, "env_done": 1 if done else 0},
             )
             experience_list.append(experience)
-        # Close the env to save cpu memory
-        env.close()
         return experience_list
 
     def run(self) -> List[Experience]:
         # assume the task_description is the session_id generated.
         session_id = int(self.task_desc)
         rollout_n = self.repeat_times
-        # TODO: Make parallel envs
-        try:
-            import gym
-            from web_agent_site.envs import WebAgentTextEnv  # noqa: F401
-        except Exception as e:
-            print("Please make sure you have installed the web_agent_site package.")
-            error_message = f"Error importing WebAgentTextEnv {str(e)}. Please make sure you have installed the web_agent_site package, following the instructions in https://github.com/princeton-nlp/WebShop"
-            raise ImportError(error_message)
-        print("Making GYM env")
-        # NOTE: Hosting the env require ~15GB CPU memory.
-        # If you want easier env, you can set the num_products to 1000 or 100000.
-        env = gym.make(
-            "WebAgentTextEnv-v0", observation_mode="text_rich", num_products=None, human_goals=True
-        )
-        return self.generate_env_inference_samples(env, session_id, rollout_n)
+        return self.generate_env_inference_samples(self.env, session_id, rollout_n)
diff --git a/trinity/common/workflows/workflow.py b/trinity/common/workflows/workflow.py
index d44d9e9813..603bd1ced4 100644
--- a/trinity/common/workflows/workflow.py
+++ b/trinity/common/workflows/workflow.py
@@ -77,6 +77,14 @@ def __init__(
         self.model = model
         self.auxiliary_models = auxiliary_models
 
+    @property
+    def resettable(self):
+        return False
+
+    def reset(self, task: Task):
+        """Reset the workflow."""
+        raise NotImplementedError
+
     @abstractmethod
     def run(self) -> List[Experience]:
         """Run workflow and return a list of experiences."""
@@ -147,6 +155,13 @@ def __init__(
             model=model,
             task=task,
         )
+        self.reset(task)
+
+    @property
+    def resettable(self):
+        return True
+
+    def reset(self, task: Task):
         self.format_args = task.format_args
         self.system_prompt = task.format_args.system_prompt
         self.reply_prefix = task.format_args.reply_prefix
diff --git a/trinity/explorer/workflow_runner.py b/trinity/explorer/workflow_runner.py
index e60821347a..f5a1c2dc6a 100644
--- a/trinity/explorer/workflow_runner.py
+++ b/trinity/explorer/workflow_runner.py
@@ -55,6 +55,7 @@ def __init__(
                 ).get_openai_client()
                 self.auxiliary_models.append(api_client)
         self.logger = get_logger(__name__)
+        self.workflow_instance = None
 
     def is_alive(self):
         return True
@@ -63,8 +64,15 @@ def _run_task(self, task: Task) -> List[Experience]:
         """Init workflow from the task and run it."""
         if task.workflow is None:
             raise ValueError("Workflow is not set in the task.")
-        workflow = task.to_workflow(self.model_wrapper, self.auxiliary_models)
-        return workflow.run()
+        if (
+            self.workflow_instance is None
+            or not self.workflow_instance.__class__ == task.workflow
+            or not self.workflow_instance.resettable
+        ):
+            self.workflow_instance = task.to_workflow(self.model_wrapper, self.auxiliary_models)
+        else:
+            self.workflow_instance.reset(task)
+        return self.workflow_instance.run()
 
     def run_task(self, task: Task) -> Status:
         """Run the task and return the states."""