diff --git a/mmengine/hooks/checkpoint_hook.py b/mmengine/hooks/checkpoint_hook.py
index bae8bd65..23f4a8ab 100644
--- a/mmengine/hooks/checkpoint_hook.py
+++ b/mmengine/hooks/checkpoint_hook.py
@@ -85,7 +85,7 @@ class CheckpointHook(Hook):
             accordingly.
         backend_args (dict, optional): Arguments to instantiate the
             prefix of uri corresponding backend. Defaults to None.
-            New in v0.2.0.
+            `New in version 0.2.0.`
         published_keys (str, List[str], optional): If ``save_last`` is ``True``
             or ``save_best`` is not ``None``, it will automatically
             publish model with keys in the list after training.
diff --git a/tests/test_hooks/test_checkpoint_hook.py b/tests/test_hooks/test_checkpoint_hook.py
index 6a80dcdd..e6469bb3 100644
--- a/tests/test_hooks/test_checkpoint_hook.py
+++ b/tests/test_hooks/test_checkpoint_hook.py
@@ -429,34 +429,37 @@ class TestCheckpointHook(RunnerTestCase):
 
     @parameterized.expand([['iter'], ['epoch']])
     def test_with_runner(self, training_type):
-        # Test interval in epoch based training
-        save_iterval = 2
-        cfg = copy.deepcopy(getattr(self, f'{training_type}_based_cfg'))
-        setattr(cfg.train_cfg, f'max_{training_type}s', 11)
+        common_cfg = getattr(self, f'{training_type}_based_cfg')
+        setattr(common_cfg.train_cfg, f'max_{training_type}s', 11)
         checkpoint_cfg = dict(
             type='CheckpointHook',
-            interval=save_iterval,
+            interval=2,
             by_epoch=training_type == 'epoch')
-        cfg.default_hooks = dict(checkpoint=checkpoint_cfg)
+        common_cfg.default_hooks = dict(checkpoint=checkpoint_cfg)
+
+        # Test interval in epoch based training
+        cfg = copy.deepcopy(common_cfg)
         runner = self.build_runner(cfg)
         runner.train()
 
         for i in range(1, 11):
-            if i == 0:
-                self.assertFalse(
-                    osp.isfile(
-                        osp.join(cfg.work_dir, f'{training_type}_{i}.pth')))
-            if i % 2 == 0:
-                self.assertTrue(
-                    osp.isfile(
-                        osp.join(cfg.work_dir, f'{training_type}_{i}.pth')))
+            self.assertEqual(
+                osp.isfile(osp.join(cfg.work_dir, f'{training_type}_{i}.pth')),
+                i % 2 == 0)
 
+        # save_last=True
         self.assertTrue(
             osp.isfile(osp.join(cfg.work_dir, f'{training_type}_11.pth')))
 
+        self.clear_work_dir()
+
         # Test save_optimizer=False
+        cfg = copy.deepcopy(common_cfg)
+        runner = self.build_runner(cfg)
+        runner.train()
         ckpt = torch.load(osp.join(cfg.work_dir, f'{training_type}_11.pth'))
         self.assertIn('optimizer', ckpt)
+
         cfg.default_hooks.checkpoint.save_optimizer = False
         runner = self.build_runner(cfg)
         runner.train()
@@ -464,6 +467,7 @@ class TestCheckpointHook(RunnerTestCase):
         self.assertNotIn('optimizer', ckpt)
 
         # Test save_param_scheduler=False
+        cfg = copy.deepcopy(common_cfg)
         cfg.param_scheduler = [
             dict(
                 type='LinearLR',
@@ -483,7 +487,10 @@ class TestCheckpointHook(RunnerTestCase):
         ckpt = torch.load(osp.join(cfg.work_dir, f'{training_type}_11.pth'))
         self.assertNotIn('param_schedulers', ckpt)
 
+        self.clear_work_dir()
+
         # Test out_dir
+        cfg = copy.deepcopy(common_cfg)
         out_dir = osp.join(self.temp_dir.name, 'out_dir')
         cfg.default_hooks.checkpoint.out_dir = out_dir
         runner = self.build_runner(cfg)
@@ -493,37 +500,54 @@ class TestCheckpointHook(RunnerTestCase):
                 osp.join(out_dir, osp.basename(cfg.work_dir),
                          f'{training_type}_11.pth')))
 
-        # Test max_keep_ckpts.
-        del cfg.default_hooks.checkpoint.out_dir
+        self.clear_work_dir()
+
+        # Test max_keep_ckpts
+        cfg = copy.deepcopy(common_cfg)
+        cfg.default_hooks.checkpoint.interval = 1
         cfg.default_hooks.checkpoint.max_keep_ckpts = 1
         runner = self.build_runner(cfg)
         runner.train()
+        print(os.listdir(cfg.work_dir))
         self.assertTrue(
-            osp.isfile(osp.join(cfg.work_dir, f'{training_type}_10.pth')))
+            osp.isfile(osp.join(cfg.work_dir, f'{training_type}_11.pth')))
 
-        for i in range(10):
+        for i in range(11):
             self.assertFalse(
                 osp.isfile(osp.join(cfg.work_dir, f'{training_type}_{i}.pth')))
 
+        self.clear_work_dir()
+
         # Test filename_tmpl
+        cfg = copy.deepcopy(common_cfg)
         cfg.default_hooks.checkpoint.filename_tmpl = 'test_{}.pth'
         runner = self.build_runner(cfg)
         runner.train()
-        self.assertTrue(osp.isfile(osp.join(cfg.work_dir, 'test_10.pth')))
+        self.assertTrue(osp.isfile(osp.join(cfg.work_dir, 'test_11.pth')))
+
+        self.clear_work_dir()
 
         # Test save_best
+        cfg = copy.deepcopy(common_cfg)
+        cfg.default_hooks.checkpoint.interval = 1
         cfg.default_hooks.checkpoint.save_best = 'test/acc'
         cfg.val_evaluator = dict(type='TriangleMetric', length=11)
         cfg.train_cfg.val_interval = 1
         runner = self.build_runner(cfg)
         runner.train()
-        self.assertTrue(
-            osp.isfile(osp.join(cfg.work_dir, 'best_test_acc_test_5.pth')))
+        best_ckpt = osp.join(cfg.work_dir,
+                             f'best_test_acc_{training_type}_5.pth')
+        self.assertTrue(osp.isfile(best_ckpt))
+
+        self.clear_work_dir()
 
         # test save published keys
+        cfg = copy.deepcopy(common_cfg)
         cfg.default_hooks.checkpoint.published_keys = ['meta', 'state_dict']
         runner = self.build_runner(cfg)
         runner.train()
         ckpt_files = os.listdir(runner.work_dir)
         self.assertTrue(
             any(re.findall(r'-[\d\w]{8}\.pth', file) for file in ckpt_files))
+
+        self.clear_work_dir()