diff --git a/RWKV-v5/src/model.py b/RWKV-v5/src/model.py
index 1e97514b..0b5f9408 100644
--- a/RWKV-v5/src/model.py
+++ b/RWKV-v5/src/model.py
@@ -915,21 +915,22 @@ def compute_loss(self, batch, batch_idx, is_training_run: bool = False, is_valid
         total_mask_sum = torch.sum(seq_mask)
         avg_mask_sum = ( total_mask_sum / B )
 
-        # Do a quick return, if there is no tokens of value to learn from due to full masking
-        if num_devices > 1 and total_mask_sum == 0:
-            return 0
+        # # Do a quick return, if there is no tokens of value to learn from due to full masking
+        # # DO NOT DO THIS : This causes multi node / multi GPU to go out of sync
+        # if num_devices <= 1 and total_mask_sum == 0:
+        #     return 0
         
         # Checkpoint steps
         def checkpointed_step(idx, targets, mask, last_shift_states,
                               last_wkv_states):
-            # Skip if there is no tokens of value to learn from
-            if idx.shape[1] == 0:
-                # Prepare dummy loss
-                train_loss = torch.tensor(0, dtype=self.emb.weight.dtype).requires_grad_()
-                sample_loss = train_loss.clone().detach().requires_grad_(False)
-
-                # Return the checkpoint values
-                return sample_loss, train_loss, last_shift_states, last_wkv_states, 0
+            # # Skip if there is no tokens of value to learn from
+            # if idx.shape[1] == 0:
+            #     # Prepare dummy loss
+            #     train_loss = torch.tensor(0, dtype=self.emb.weight.dtype).requires_grad_()
+            #     sample_loss = train_loss.clone().detach().requires_grad_(False)
+
+            #     # Return the checkpoint values
+            #     return sample_loss, train_loss, last_shift_states, last_wkv_states, 0
 
             # Get the logits, and the new states
             logits, new_shift_states, new_wkv_states = self(