diff --git a/mace/tools/train.py b/mace/tools/train.py
index 32d33588..a3f73ff9 100644
--- a/mace/tools/train.py
+++ b/mace/tools/train.py
@@ -209,6 +209,7 @@ def train(
                     output_args=output_args,
                     device=device,
                 )
+            if rank == 0:
                 valid_err_log(
                     valid_loss,
                     eval_metrics,
@@ -216,29 +217,42 @@ def train(
                     log_errors,
                     epoch,
                 )
-
-            if log_wandb:
-                wandb_log_dict = {
-                    "epoch": epoch,
-                    "valid_loss": valid_loss,
-                    "valid_rmse_e_per_atom": eval_metrics["rmse_e_per_atom"],
-                    "valid_rmse_f": eval_metrics["rmse_f"],
-                }
-                wandb.log(wandb_log_dict)
-
-            if valid_loss >= lowest_loss:
-                patience_counter += 1
-                if patience_counter >= patience and epoch < swa.start:
-                    logging.info(
-                        f"Stopping optimization after {patience_counter} epochs without improvement and starting swa"
-                    )
-                    epoch = swa.start
-                elif patience_counter >= patience and epoch >= swa.start:
-                    logging.info(
-                        f"Stopping optimization after {patience_counter} epochs without improvement"
-                    )
-                    break
-                if save_all_checkpoints:
+                if log_wandb:
+                    wandb_log_dict = {
+                        "epoch": epoch,
+                        "valid_loss": valid_loss,
+                        "valid_rmse_e_per_atom": eval_metrics["rmse_e_per_atom"],
+                        "valid_rmse_f": eval_metrics["rmse_f"],
+                    }
+                    wandb.log(wandb_log_dict)
+
+                if valid_loss >= lowest_loss:
+                    patience_counter += 1
+                    if patience_counter >= patience and epoch < swa.start:
+                        logging.info(
+                            f"Stopping optimization after {patience_counter} epochs without improvement and starting swa"
+                        )
+                        epoch = swa.start
+                    elif patience_counter >= patience and epoch >= swa.start:
+                        logging.info(
+                            f"Stopping optimization after {patience_counter} epochs without improvement"
+                        )
+                        break
+                    if save_all_checkpoints:
+                        param_context = (
+                            ema.average_parameters()
+                            if ema is not None
+                            else nullcontext()
+                        )
+                        with param_context:
+                            checkpoint_handler.save(
+                                state=CheckpointState(model, optimizer, lr_scheduler),
+                                epochs=epoch,
+                                keep_last=True,
+                            )
+                else:
+                    lowest_loss = valid_loss
+                    patience_counter = 0
                     param_context = (
                         ema.average_parameters() if ema is not None else nullcontext()
                     )
@@ -246,21 +260,9 @@ def train(
                         checkpoint_handler.save(
                             state=CheckpointState(model, optimizer, lr_scheduler),
                             epochs=epoch,
-                            keep_last=True,
+                            keep_last=keep_last,
                         )
-            else:
-                lowest_loss = valid_loss
-                patience_counter = 0
-                param_context = (
-                    ema.average_parameters() if ema is not None else nullcontext()
-                )
-                with param_context:
-                    checkpoint_handler.save(
-                        state=CheckpointState(model, optimizer, lr_scheduler),
-                        epochs=epoch,
-                        keep_last=keep_last,
-                    )
-                    keep_last = False or save_all_checkpoints
+                        keep_last = False or save_all_checkpoints
         if distributed:
             torch.distributed.barrier()
         epoch += 1