diff --git a/python/tvm/auto_scheduler/search_policy.py b/python/tvm/auto_scheduler/search_policy.py
index 6f565edbd378..5b15a48943d2 100644
--- a/python/tvm/auto_scheduler/search_policy.py
+++ b/python/tvm/auto_scheduler/search_policy.py
@@ -57,7 +57,7 @@ class PreloadMeasuredStates(SearchCallback):
         The name of the record file.
     """
 
-    def __init__(self, filename="auto_scheduler_tuning.json"):
+    def __init__(self, filename):
         self.__init_handle_by_constructor__(_ffi_api.PreloadMeasuredStates, filename)
 
 
diff --git a/python/tvm/auto_scheduler/task_scheduler.py b/python/tvm/auto_scheduler/task_scheduler.py
index a3dbcae64b60..ab83ff40c461 100644
--- a/python/tvm/auto_scheduler/task_scheduler.py
+++ b/python/tvm/auto_scheduler/task_scheduler.py
@@ -29,7 +29,7 @@
 
 import numpy as np
 
-from .search_policy import SearchPolicy, SketchPolicy
+from .search_policy import SearchPolicy, SketchPolicy, PreloadMeasuredStates
 from .cost_model import RandomModel, XGBModel
 from .utils import array_mean
 from .measure import ProgramMeasurer
@@ -94,8 +94,19 @@ def make_search_policies(
             raise ValueError("Invalid search policy: " + search_policy)
 
         if policy_type == "sketch":
+            if load_log_file:
+                # use the log file to restore the status of search policies.
+                init_search_callbacks = [PreloadMeasuredStates(load_log_file)]
+            else:
+                init_search_callbacks = None
             search_policies = [
-                SketchPolicy(task, cost_model, params=search_policy_params, verbose=verbose)
+                SketchPolicy(
+                    task,
+                    cost_model,
+                    params=search_policy_params,
+                    verbose=verbose,
+                    init_search_callbacks=init_search_callbacks,
+                )
                 for task in tasks
             ]
         else:
diff --git a/tutorials/auto_scheduler/tune_network_cuda.py b/tutorials/auto_scheduler/tune_network_cuda.py
index 03be05abd363..3da9f3fc577b 100644
--- a/tutorials/auto_scheduler/tune_network_cuda.py
+++ b/tutorials/auto_scheduler/tune_network_cuda.py
@@ -299,10 +299,14 @@ def run_tuning():
 # 1. During the tuning, the auto-scheduler needs to compile many programs and
 #    extract feature from them. This part is CPU-intensive,
 #    so a high-performance CPU with many cores is recommended for faster search.
-# 2. If you have multiple target GPUs, you can use all of them for measurements to
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
+#    to distill the large log file and only save the best useful records.
+# 3. You can resume a search from the previous log file. You just need to
+#    add a new argument :code:`load_log_file` when creating the task scheduler
+#    in function :code:`run_tuning`. Say,
+#    :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
+# 4. If you have multiple target GPUs, you can use all of them for measurements to
 #    parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
 #    to learn how to use the RPC Tracker and RPC Server.
 #    To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
 #    with :any:`auto_scheduler.RPCRunner`.
-# 3. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
-#    to distill the large log file and only save the best useful records.
diff --git a/tutorials/auto_scheduler/tune_network_x86.py b/tutorials/auto_scheduler/tune_network_x86.py
index aba75b253e0c..a491759ab128 100644
--- a/tutorials/auto_scheduler/tune_network_x86.py
+++ b/tutorials/auto_scheduler/tune_network_x86.py
@@ -298,10 +298,14 @@ def run_tuning():
 # 1. During the tuning, the auto-scheduler needs to compile many programs and
 #    extract feature from them. This part is CPU-intensive,
 #    so a high-performance CPU with many cores is recommended for faster search.
-# 2. If you have multiple target CPUs, you can use all of them for measurements to
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
+#    to distill the large log file and only save the best useful records.
+# 3. You can resume a search from the previous log file. You just need to
+#    add a new argument :code:`load_log_file` when creating the task scheduler
+#    in function :code:`run_tuning`. Say,
+#    :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
+# 4. If you have multiple target CPUs, you can use all of them for measurements to
 #    parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
 #    to learn how to use the RPC Tracker and RPC Server.
 #    To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
 #    with :any:`auto_scheduler.RPCRunner`.
-# 3. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
-#    to distill the large log file and only save the best useful records.