diff --git a/lib/vggflow/trainer.py b/lib/vggflow/trainer.py
index 7194a46..c560d73 100644
--- a/lib/vggflow/trainer.py
+++ b/lib/vggflow/trainer.py
@@ -320,7 +320,7 @@ def _aggregate_and_log_metrics(self, global_step, num_processes):
                 aggregated_info[k] = torch.max(torch.stack(v))
             elif '_all' in k:
                 aggregated_info[k] = torch.stack(v)
-                cache[k] = [torch.zeros_like(aggregated_info[k])] * num_processes
+                cache[k] = [torch.zeros_like(aggregated_info[k]) for _ in range(num_processes)]
             else:
                 aggregated_info[k] = torch.mean(torch.stack(v))
 
@@ -334,45 +334,49 @@ def _aggregate_and_log_metrics(self, global_step, num_processes):
                 dist.all_reduce(v, op=dist.ReduceOp.MAX)
             elif '_median' in k:
                 dist.all_gather(cache[k], v)
-                new_info[k.replace('_all', '')] = torch.median(cache[k][self.local_rank])
+                gathered = torch.cat([t.reshape(-1) for t in cache[k]], dim=0)
+                new_info[k.replace('_all', '')] = torch.median(gathered)
             elif '_08quantile' in k:
                 dist.all_gather(cache[k], v)
-                new_info[k.replace('_all', '')] = torch.quantile(cache[k][self.local_rank], 0.8)
+                gathered = torch.cat([t.reshape(-1) for t in cache[k]], dim=0)
+                new_info[k.replace('_all', '')] = torch.quantile(gathered, 0.8)
             elif '_std' in k:
                 dist.all_gather(cache[k], v)
-                new_info[k.replace('_all', '')] = torch.std(cache[k][self.local_rank])
+                gathered = torch.cat([t.reshape(-1) for t in cache[k]], dim=0)
+                new_info[k.replace('_all', '')] = torch.std(gathered)
             else:
                 dist.all_reduce(v, op=dist.ReduceOp.SUM)
 
-        # Remove '_all' keys and add computed statistics
-        for k in list(aggregated_info.keys()):
+        # Average reduced means across processes; keep min/max as-is; drop raw "_all" tensors.
+        final_info = {}
+        for k, v in aggregated_info.items():
             if '_all' in k:
-                aggregated_info.pop(k, None)
-        aggregated_info.update(new_info)
+                continue
+            if '_min' in k or '_max' in k:
+                final_info[k] = v
+            else:
+                final_info[k] = v / num_processes
 
-        # Average across processes
-        aggregated_info = {
-            k: v / num_processes if ('_min' not in k and '_max' not in k) else v
-            for k, v in aggregated_info.items()
-        }
+        # Add computed statistics (already global across all ranks; must not be divided)
+        final_info.update(new_info)
 
         # Update rgrad threshold
-        rgrad_threshold = aggregated_info.get('rgrad_08quantile', 1.0).item()
+        rgrad_threshold = final_info.get('rgrad_08quantile', 1.0).item()
 
         # Add epoch and step info
         if self._is_main_process():
             if self.scaler:
-                aggregated_info["grad_scale"] = self.scaler.get_scale()
-            aggregated_info["global_step"] = float(global_step)
+                final_info["grad_scale"] = self.scaler.get_scale()
+            final_info["global_step"] = float(global_step)
 
             # Log to wandb
             if self.config.logging.use_wandb:
                 import wandb
-                wandb.log(aggregated_info, step=global_step)
+                wandb.log(final_info, step=global_step)
 
             # Log to console
             self.logger.info(f"global_step={global_step}  " +
-                           " ".join([f"{k}={v:.6f}" for k, v in aggregated_info.items()]))
+                           " ".join([f"{k}={v:.6f}" for k, v in final_info.items()]))
 
         # Reset info dict
         self.info = defaultdict(list)