diff --git a/dizoo/atari/entry/pong_cql_main.py b/dizoo/atari/entry/pong_cql_main.py
index a4b4383ec1..fca48a1ff6 100644
--- a/dizoo/atari/entry/pong_cql_main.py
+++ b/dizoo/atari/entry/pong_cql_main.py
@@ -1,7 +1,6 @@
 import torch
 from copy import deepcopy
 
-from dizoo.atari.config.serial.pong.pong_qrdqn_generation_data_config import main_config, create_config
 from ding.entry import serial_pipeline_offline, collect_demo_data, eval, serial_pipeline
 
 
@@ -15,22 +14,21 @@ def train_cql(args):
 
 
 def eval_ckpt(args):
+    from dizoo.atari.config.serial.pong.pong_qrdqn_generation_data_config import main_config, create_config
     main_config.exp_name = 'pong'
-    main_config.policy.learn.learner.load_path = './pong/ckpt/ckpt_best.pth.tar'
-    main_config.policy.learn.learner.hook.load_ckpt_before_run = './pong/ckpt/ckpt_best.pth.tar'
     config = deepcopy([main_config, create_config])
-    eval(config, seed=args.seed, load_path=main_config.policy.learn.learner.hook.load_ckpt_before_run)
+    eval(config, seed=args.seed, load_path='./pong/ckpt/ckpt_best.pth.tar')
 
 
 def generate(args):
+    from dizoo.atari.config.serial.pong.pong_qrdqn_generation_data_config import main_config, create_config
     main_config.exp_name = 'pong'
-    main_config.policy.learn.learner.load_path = './pong/ckpt/ckpt_best.pth.tar'
     main_config.policy.collect.save_path = './pong/expert.pkl'
     config = deepcopy([main_config, create_config])
-    state_dict = torch.load(main_config.policy.learn.learner.load_path, map_location='cpu')
+    state_dict = torch.load('./pong/ckpt/ckpt_best.pth.tar', map_location='cpu')
     collect_demo_data(
         config,
-        collect_count=main_config.policy.other.replay_buffer.replay_buffer_size,
+        collect_count=int(1e5),
         seed=args.seed,
         expert_data_path=main_config.policy.collect.save_path,
         state_dict=state_dict
diff --git a/dizoo/classic_control/cartpole/entry/cartpole_cql_main.py b/dizoo/classic_control/cartpole/entry/cartpole_cql_main.py
index 5610a879be..311a2c7c11 100644
--- a/dizoo/classic_control/cartpole/entry/cartpole_cql_main.py
+++ b/dizoo/classic_control/cartpole/entry/cartpole_cql_main.py
@@ -1,7 +1,6 @@
 import torch
 from copy import deepcopy
 
-from dizoo.classic_control.cartpole.config.cartpole_qrdqn_generation_data_config import main_config, create_config
 from ding.entry import serial_pipeline_offline, collect_demo_data, eval, serial_pipeline
 
 
@@ -15,23 +14,23 @@ def train_cql(args):
 
 
 def eval_ckpt(args):
+    from dizoo.classic_control.cartpole.config.cartpole_qrdqn_config import main_config, create_config
+    main_config, create_config = deepcopy(main_config), deepcopy(create_config)
     main_config.exp_name = 'cartpole'
-    main_config.policy.learn.learner.load_path = './cartpole/ckpt/ckpt_best.pth.tar'
-    main_config.policy.learn.learner.hook.load_ckpt_before_run = './cartpole/ckpt/ckpt_best.pth.tar'
     config = deepcopy([main_config, create_config])
-    eval(config, seed=args.seed, load_path=main_config.policy.learn.learner.hook.load_ckpt_before_run)
+    eval(config, seed=args.seed, load_path='./cartpole/ckpt/ckpt_best.pth.tar')
 
 
 def generate(args):
+    from dizoo.classic_control.cartpole.config.cartpole_qrdqn_generation_data_config import main_config, create_config
     main_config.exp_name = 'cartpole'
-    main_config.policy.learn.learner.load_path = './cartpole/ckpt/ckpt_best.pth.tar'
     main_config.policy.collect.save_path = './cartpole/expert.pkl'
     main_config.policy.collect.data_type = 'hdf5'
     config = deepcopy([main_config, create_config])
-    state_dict = torch.load(main_config.policy.learn.learner.load_path, map_location='cpu')
+    state_dict = torch.load('./cartpole/ckpt/ckpt_best.pth.tar', map_location='cpu')
     collect_demo_data(
         config,
-        collect_count=main_config.policy.other.replay_buffer.replay_buffer_size,
+        collect_count=10000,
         seed=args.seed,
         expert_data_path=main_config.policy.collect.save_path,
         state_dict=state_dict
@@ -40,6 +39,7 @@ def generate(args):
 
 def train_expert(args):
     from dizoo.classic_control.cartpole.config.cartpole_qrdqn_config import main_config, create_config
+    main_config, create_config = deepcopy(main_config), deepcopy(create_config)
     main_config.exp_name = 'cartpole'
     config = deepcopy([main_config, create_config])
     serial_pipeline(config, seed=args.seed)