diff --git a/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py b/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py
index 83a51b757..28828b36d 100644
--- a/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py
+++ b/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py
@@ -59,7 +59,7 @@ class CachedParamMgr(torch.nn.Module):
         """_update_freq_cnter 
 
         Update the frequency valude w.r.t. the cpu_row_ids in self.freq_cnter.
-
+        
         Args:
             cpu_row_idxs (torch.Tensor): a list of indices of cpu weight.
         """
@@ -80,7 +80,7 @@ class CachedParamMgr(torch.nn.Module):
         if self._evict_strategy == EvictionStrategy.LFU:
             # find the minimal evict_num freq entries in cached_idx_map
             evict_gpu_row_idxs = torch.argsort(self.freq_cnter[self.cached_idx_map])[:evict_num]
-            return self.cached_idx_map[evict_gpu_row_idxs]
+            return evict_gpu_row_idxs
         elif self._evict_strategy == EvictionStrategy.DATASET:
             # cached_idx_map itself implies the priority of eviction.
             # The value of self.cached_idx_map represents cpu_row_idx.
@@ -298,15 +298,27 @@ class CachedParamMgr(torch.nn.Module):
         if evict_num > 0:
             with Timer() as timer:
                 mask_cpu_row_idx = torch.isin(self.cached_idx_map, self.evict_backlist)
-                backup_idxs = self.cached_idx_map[mask_cpu_row_idx].clone()
+                
                 invalid_idxs = torch.nonzero(mask_cpu_row_idx).squeeze(1)
 
-                self.cached_idx_map.index_fill_(0, invalid_idxs, -2)
-
-                evict_gpu_row_idxs = self._find_evict_gpu_idxs(evict_num)
-
-                self.cached_idx_map.index_copy_(0, invalid_idxs, backup_idxs)
-
+                if self._evict_strategy == EvictionStrategy.DATASET:
+                    # mask method. 
+                    # set cached_idx_map[invalid_idxs] to -2.
+                    # so those idxs will be sorted to end, therefore not being chosen as victim
+                    backup_idxs = self.cached_idx_map[mask_cpu_row_idx].clone()
+                    self.cached_idx_map.index_fill_(0, invalid_idxs, -2)
+                    evict_gpu_row_idxs = self._find_evict_gpu_idxs(evict_num)
+                    self.cached_idx_map.index_copy_(0, invalid_idxs, backup_idxs)
+                    
+                elif self._evict_strategy == EvictionStrategy.LFU:
+                    # another mask method.
+                    # set freq_cnter[invalid_idxs] to max
+                    # so those idxs will be sorted to end, therefore not being chosen as victim
+                    backup_cnter = self.freq_cnter[invalid_idxs].clone()
+                    self.freq_cnter.index_fill_(0, invalid_idxs, torch.max(self.freq_cnter) + 1) # or can we use a confident max value? 
+                    evict_gpu_row_idxs = self._find_evict_gpu_idxs(evict_num)
+                    self.freq_cnter.index_copy_(0,invalid_idxs,backup_cnter)
+                    
                 evict_info = self.cached_idx_map[evict_gpu_row_idxs]
 
                 if self.buffer_size > 0:
diff --git a/tests/test_layers/test_cache_embedding.py b/tests/test_layers/test_cache_embedding.py
index 71c22e243..99caf1407 100644
--- a/tests/test_layers/test_cache_embedding.py
+++ b/tests/test_layers/test_cache_embedding.py
@@ -144,6 +144,44 @@ def test_freq_aware_embed(use_LFU: bool):
     assert torch.allclose(model_weight, ref_weight), \
         f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}"
 
+def test_lfu_strategy():
+    # minimal test to check behavior
+    Bag = FreqAwareEmbeddingBag(
+        5,
+        5,
+        cuda_row_num=3,
+        buffer_size=0,
+        pin_weight=True,
+        warmup_ratio=0.0,
+        evict_strategy=EvictionStrategy.LFU
+    )
+
+    offsets = torch.tensor([0],device="cuda:0")
+
+    # prepare frequency learning info:
+    Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([0],device="cuda:0"),offsets)
+
+    # check strategy
+    Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets)
+    Bag.forward(torch.tensor([3],device="cuda:0"),offsets) # miss, evict 1
+    Bag.forward(torch.tensor([2],device="cuda:0"),offsets) # hit
+    Bag.forward(torch.tensor([4],device="cuda:0"),offsets) # miss, evict 1
+    Bag.forward(torch.tensor([2],device="cuda:0"),offsets) # hit 
+    Bag.forward(torch.tensor([0],device="cuda:0"),offsets) # hit
+
+    assert torch.allclose(torch.Tensor(Bag.cache_weight_mgr.num_hits_history[-6:]), torch.Tensor([3, 0, 1, 0, 1, 1])), \
+        "LFU strategy behavior failed"
 
 def gather_tensor(tensor, rank, world_size):
     gather_list = []
@@ -237,3 +275,4 @@ def test_parallel_freq_aware_embed(world_size):
 if __name__ == '__main__':
     test_freq_aware_embed(True)
     # test_parallel_freq_aware_embed(2)
+    # test_lfu_strategy()
\ No newline at end of file