diff --git a/kronfluence/computer/score_computer.py b/kronfluence/computer/score_computer.py
index 74699fb..d606124 100644
--- a/kronfluence/computer/score_computer.py
+++ b/kronfluence/computer/score_computer.py
@@ -450,6 +450,7 @@ def compute_pairwise_scores(
                         )
                     self.state.wait_for_everyone()
                 del scores, query_loader, train_loader
+                self._reset_memory()
                 self.logger.info(f"Saved pairwise scores at {scores_output_dir}.")
 
         all_end_time = get_time(state=self.state)
@@ -755,6 +756,7 @@ def compute_self_scores(
                         )
                     self.state.wait_for_everyone()
                 del scores, train_loader
+                self._reset_memory()
                 self.logger.info(f"Saved self-influence scores at `{scores_output_dir}`.")
 
         all_end_time = get_time(state=self.state)
diff --git a/kronfluence/score/pairwise.py b/kronfluence/score/pairwise.py
index 2a993cb..65b79b1 100644
--- a/kronfluence/score/pairwise.py
+++ b/kronfluence/score/pairwise.py
@@ -192,6 +192,7 @@ def compute_pairwise_scores_with_loaders(
                 factors=loaded_factors[name],
             )
     prepare_modules(model=model, tracked_module_names=tracked_module_names, device=state.device)
+    release_memory()
 
     total_scores_chunks: Dict[str, Union[List[torch.Tensor], torch.Tensor]] = {}
     total_query_batch_size = per_device_query_batch_size * state.num_processes
diff --git a/kronfluence/utils/state.py b/kronfluence/utils/state.py
index c145255..8f4da96 100644
--- a/kronfluence/utils/state.py
+++ b/kronfluence/utils/state.py
@@ -1,7 +1,7 @@
 import contextlib
 import gc
 import os
-from typing import Any, Callable, Dict
+from typing import Any, Callable, Dict, List
 
 import torch
 import torch.distributed as dist
@@ -113,6 +113,18 @@ def release_memory() -> None:
         torch.cuda.empty_cache()
 
 
+def get_active_tensors() -> List[torch.Tensor]:
+    # https://discuss.pytorch.org/t/how-to-debug-causes-of-gpu-memory-leaks/6741/3
+    tensor_lst = []
+    for obj in gc.get_objects():
+        try:
+            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
+                tensor_lst.append(type(obj), obj.size())
+        except:
+            pass
+    return tensor_lst
+
+
 @contextlib.contextmanager
 def no_sync(model: nn.Module, state: State) -> Callable:
     """A context manager to temporarily disable gradient synchronization in distributed setting.