diff --git a/qmb/haar.py b/qmb/haar.py
index a7211da..81170b7 100644
--- a/qmb/haar.py
+++ b/qmb/haar.py
@@ -16,6 +16,7 @@
 from .subcommand_dict import subcommand_dict
 from .model_dict import ModelProto
 from .optimizer import initialize_optimizer, scale_learning_rate
+from .bitspack import pack_int
 
 
 @dataclasses.dataclass
@@ -256,6 +257,8 @@ class HaarConfig:
 
     common: typing.Annotated[CommonConfig, tyro.conf.OmitArgPrefixes]
 
+    # The initial configurations for the first step
+    initial_config: typing.Annotated[str, tyro.conf.arg(aliases=["-i"])] = ""
     # The sampling count from neural network
     sampling_count_from_neural_network: typing.Annotated[int, tyro.conf.arg(aliases=["-n"])] = 1024
     # The sampling count from last iteration
@@ -364,6 +367,17 @@ def main(self, *, model_param: typing.Any = None, network_param: typing.Any = No
             pool_configs, pool_psi = data["haar"]["pool"]
             data["haar"]["pool"] = (pool_configs.to(device=self.common.device), pool_psi.to(device=self.common.device))
 
+        if self.initial_config != "":
+            if data["haar"]["pool"] is None:
+                config = pack_int(
+                    torch.tensor([[int(i) for i in single_config] for single_config in self.initial_config.split(",")], dtype=torch.uint8, device=self.common.device),
+                    size=1,
+                )
+                data["haar"]["pool"] = (config, network(config))
+                logging.info("The initial configuration is imported successfully.")
+            else:
+                logging.info("The initial configuration is provided, but the pool from the last iteration is not empty, so the initial configuration will be ignored.")
+
         writer = torch.utils.tensorboard.SummaryWriter(log_dir=self.common.folder())  # type: ignore[no-untyped-call]
 
         while True: