diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 49e780306611dd..e1871eaf8c3583 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -2175,6 +2175,9 @@ def _inner_training_loop(
         grad_norm: Optional[float] = None
         self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
 
+        if args.sanity_evaluation:
+            self._evaluate(trial, ignore_keys_for_eval, skip_scheduler=True)
+
         total_batched_samples = 0
         for epoch in range(epochs_trained, num_train_epochs):
             epoch_iterator = train_dataloader
@@ -2723,6 +2726,18 @@ def _issue_warnings_after_load(self, load_result):
                 f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
             )
 
+    def _evaluate(self, trial, ignore_keys_for_eval, skip_scheduler=False):
+        metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
+        self._report_to_hp_search(trial, self.state.global_step, metrics)
+
+        # Run delayed LR scheduler now that metrics are populated
+        if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau) and not skip_scheduler:
+            metric_to_check = self.args.metric_for_best_model
+            if not metric_to_check.startswith("eval_"):
+                metric_to_check = f"eval_{metric_to_check}"
+            self.lr_scheduler.step(metrics[metric_to_check])
+        return metrics
+
     def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval):
         if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
             if is_torch_xla_available():
@@ -2749,15 +2764,7 @@ def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, igno
 
         metrics = None
         if self.control.should_evaluate:
-            metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
-            self._report_to_hp_search(trial, self.state.global_step, metrics)
-
-            # Run delayed LR scheduler now that metrics are populated
-            if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
-                metric_to_check = self.args.metric_for_best_model
-                if not metric_to_check.startswith("eval_"):
-                    metric_to_check = f"eval_{metric_to_check}"
-                self.lr_scheduler.step(metrics[metric_to_check])
+            metrics = self._evaluate(trial, ignore_keys_for_eval)
 
         if self.control.should_save:
             self._save_checkpoint(model, trial, metrics=metrics)
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index a97139a07ba938..b506e4b1507320 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -771,6 +771,9 @@ class TrainingArguments:
             rather than saving all eval logits in memory. When set to `True`, you must pass a compute_metrics function
             that takes a boolean argument `compute_result`, which when passed `True`, will trigger the final global
             summary statistics from the batch-level summary statistics you've accumulated over the evaluation set.
+
+        sanity_evaluation(`bool`, *optional*, defaults to `False`):
+            Whether or not to perform a sanity check to ensure that the validation steps works correctly. It will be performed before the training.
     """
 
     framework = "pt"
@@ -1454,6 +1457,13 @@ class TrainingArguments:
         metadata={"help": "Break eval metrics calculation into batches to save memory."},
     )
 
+    sanity_evaluation: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check."
+        },
+    )
+
     def __post_init__(self):
         # Parse in args that could be `dict` sent in from the CLI as a string
         for field in _VALID_DICT_FIELDS: