diff --git a/environments/community/pytorch_optimizer_coding/FOB/pytorch_fob/engine/run.py b/environments/community/pytorch_optimizer_coding/FOB/pytorch_fob/engine/run.py
index c33970ea..03f1695c 100644
--- a/environments/community/pytorch_optimizer_coding/FOB/pytorch_fob/engine/run.py
+++ b/environments/community/pytorch_optimizer_coding/FOB/pytorch_fob/engine/run.py
@@ -81,7 +81,7 @@ class Run:
         self.run_dir.mkdir(parents=True, exist_ok=True)
         self.export_config()
         scores: dict[str, _EVALUATE_OUTPUT] = {}
-        if any([self.engine.train, self.engine.test]):
+        if any([self.engine.train, self.engine.test, self.engine.validate]):
             self._ensure_resume_path()
             self.ensure_max_steps()
             torch.set_float32_matmul_precision("high")
@@ -94,7 +94,8 @@ class Run:
                 "optimizer_time"
             ].total_mean_optimizer_step_time_ms
         if self.engine.validate:
-            scores["validation"] = self._validate(trainer, model, data_module)
+            validator = self.get_validator()
+            scores["validation"] = self._validate(validator, model, data_module)
         if self.engine.test:
             tester = self.get_tester()
             if (
@@ -151,9 +152,9 @@ class Run:
             f.write(str(train_time) + "\n")
 
     def _validate(
-        self, trainer: Trainer, model: LightningModule, data_module: LightningDataModule
+        self, validator: Trainer, model: LightningModule, data_module: LightningDataModule
     ) -> _EVALUATE_OUTPUT:
-        score = trainer.validate(model, datamodule=data_module)
+        score = validator.validate(model, datamodule=data_module)
         return score
 
     def _test(
@@ -233,6 +234,16 @@ class Run:
             accelerator=self.engine.accelerator,
         )
 
+    def get_validator(self) -> Trainer:
+        return Trainer(
+            devices=1,
+            logger=False,
+            enable_progress_bar=(not self.engine.silent),
+            deterministic=self.engine.deterministic,
+            precision=precision_with_fallback(self.engine.precision),  # type: ignore
+            accelerator=self.engine.accelerator,
+        )
+
     def get_best_checkpoint(self) -> Optional[Path]:
         model_checkpoint = self._callbacks.get("best_model_checkpoint", None)
         if model_checkpoint is not None: