diff --git a/src/lightning/pytorch/trainer/trainer.py b/src/lightning/pytorch/trainer/trainer.py
index 6436fc54b7bed..291daba7b4dc8 100644
--- a/src/lightning/pytorch/trainer/trainer.py
+++ b/src/lightning/pytorch/trainer/trainer.py
@@ -541,6 +541,7 @@ def fit(
         self.state.fn = TrainerFn.FITTING
         self.state.status = TrainerStatus.RUNNING
         self.training = True
+        self.should_stop = False
         call._call_and_handle_interrupt(
             self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
         )
diff --git a/tests/tests_pytorch/loops/test_training_epoch_loop.py b/tests/tests_pytorch/loops/test_training_epoch_loop.py
index 16ed3842e3a96..ffb0044cfdc54 100644
--- a/tests/tests_pytorch/loops/test_training_epoch_loop.py
+++ b/tests/tests_pytorch/loops/test_training_epoch_loop.py
@@ -17,7 +17,7 @@
 import pytest
 import torch
 from lightning.fabric.utilities.warnings import PossibleUserWarning
-from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.trainer.trainer import Trainer
 from lightning_utilities.test.warning import no_warning_call
@@ -90,7 +90,16 @@ def test_should_stop_triggers_validation_once(min_epochs, min_steps, val_count,
     (min_epochs/steps is satisfied).
 
     """
-    model = BoringModel()
+
+    class NewBoring(BoringModel):
+        def training_step(self, batch, batch_idx):
+            self.log("loss", self.step(batch))
+            return {"loss": self.step(batch)}
+
+    model = NewBoring()
+    # create a stopping condition with a high threshold so it triggers immediately
+    # check the condition before validation so the count is unaffected
+    stopping = EarlyStopping(monitor="loss", check_on_train_epoch_end=True, stopping_threshold=100)
     trainer = Trainer(
         default_root_dir=tmp_path,
         num_sanity_val_steps=0,
@@ -101,8 +110,8 @@ def test_should_stop_triggers_validation_once(min_epochs, min_steps, val_count,
         min_steps=min_steps,
         enable_model_summary=False,
         enable_checkpointing=False,
+        callbacks=[stopping],
     )
-    trainer.should_stop = True  # Request to stop before min_epochs/min_steps are reached
     trainer.fit_loop.epoch_loop.val_loop.run = Mock()
     trainer.fit(model)
     assert trainer.fit_loop.epoch_loop.val_loop.run.call_count == val_count