diff --git a/docs/source/concept_guides/deferring_execution.md b/docs/source/concept_guides/deferring_execution.md
index 22e6a21cb0b..c2cc55a4239 100644
--- a/docs/source/concept_guides/deferring_execution.md
+++ b/docs/source/concept_guides/deferring_execution.md
@@ -111,16 +111,17 @@ with accelerator.main_process_first():
 
 ## Applying checks such as Early Stopping
 
-To have a check that works with a flag set by a particular process, the `check` and `set` breakpoint API should be used.
+To have a check that works with a flag set by a particular process, the `check` and `set` breakpoint API should be used. Useful examples
+for doing so can include situations such as using early stopping and monitoring the loss (as each loss slightly differs on each process).
 
-Call [`Accelerator.set_breakpoint`] when your condition has been met, and [`Accelerator.check_breakpoint`] when checking if that condition has been met in any process:
+Call [`Accelerator.set_trigger`] when your condition has been met, and [`Accelerator.check_trigger`] when checking if that condition has been met in any process:
 
 ```python
-# Assume `should_do_breakpoint` is a custom defined function that returns a conditional
-if should_do_breakpoint(loss):
-    accelerator.set_breakpoint()
+# Assume `should_do_early_stopping` is a custom defined function that returns a conditional
+if should_do_early_stopping(loss):
+    accelerator.set_trigger()
 
 # Later in the training script when we need to check for the breakpoint
-if accelerator.check_breakpoint():
+if accelerator.check_trigger():
     break
-```
+```
\ No newline at end of file
diff --git a/examples/by_feature/early_stopping.py b/examples/by_feature/early_stopping.py
index cfa6528ec40..e9c5e2ccbd0 100644
--- a/examples/by_feature/early_stopping.py
+++ b/examples/by_feature/early_stopping.py
@@ -197,6 +197,15 @@ def training_function(config, args):
                 lr_scheduler.step()
                 optimizer.zero_grad()
 
+            # New code
+            # Check if we should stop the training on any processes
+            if callback.check_early_stopping(loss.item()):
+                accelerator.set_trigger()
+
+            # If so, we break the loop
+            if accelerator.check_trigger():
+                break
+
         model.eval()
         for step, batch in enumerate(eval_dataloader):
             # We could avoid this line since we set the accelerator with `device_placement=True`.
@@ -212,15 +221,6 @@ def training_function(config, args):
 
         eval_metric = metric.compute()
 
-        # New code
-        # Check if we should stop the training on any processes
-        if callback.check_early_stopping(outputs.loss.item()):
-            accelerator.set_breakpoint()
-
-        # If so, we break the loop
-        if accelerator.check_breakpoint():
-            break
-
         # Use accelerator.print to print only on the main process.
         accelerator.print(f"epoch {epoch}:", eval_metric)
 
diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
index a911a6a6637..2d20ba716b8 100755
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -1966,10 +1966,10 @@ def backward(self, loss, **kwargs):
         else:
             loss.backward(**kwargs)
 
-    def set_breakpoint(self):
+    def set_trigger(self):
         """
-        Sets the internal flag tensor to 1 on the current process. A latter check of `Accelerator().check_breakpoint()`
-        should follow using this which will check across all processes.
+        Sets the internal trigger tensor to 1 on the current process. A latter check should follow using this which
+        will check across all processes.
 
         Note:
             Does not require `wait_for_everyone()`
@@ -1984,7 +1984,7 @@ def set_breakpoint(self):
         >>> # `should_do_breakpoint` is a custom function to monitor when to break,
         >>> # e.g. when the loss is NaN
         >>> if should_do_breakpoint(loss):
-        ...     accelerator.set_breakpoint()
+        ...     accelerator.set_trigger()
         >>> # Assume later in the training script
         >>> if accelerator.check_breakpoint():
         ...     break
@@ -1992,10 +1992,10 @@ def set_breakpoint(self):
         """
         self.flag_tensor = torch.tensor(1, device=self.device)
 
-    def check_breakpoint(self):
+    def check_trigger(self):
         """
-        Checks if `self.flag_tensor` has been set to 1 in any of the processes. If so, will return `True` and reset the
-        flag tensor to 0.
+        Checks if the internal trigger tensor has been set to 1 in any of the processes. If so, will return `True` and
+        reset the trigger tensor to 0.
 
         Note:
             Does not require `wait_for_everyone()`
@@ -2010,9 +2010,9 @@ def check_breakpoint(self):
         >>> # `should_do_breakpoint` is a custom function to monitor when to break,
         >>> # e.g. when the loss is NaN
         >>> if should_do_breakpoint(loss):
-        ...     accelerator.set_breakpoint()
+        ...     accelerator.set_trigger()
         >>> # Assume later in the training script
-        >>> if accelerator.check_breakpoint():
+        >>> if accelerator.check_trigger():
         ...     break
         ```
         """
diff --git a/src/accelerate/test_utils/scripts/test_script.py b/src/accelerate/test_utils/scripts/test_script.py
index 93b6ce660e0..82dbe2cfb80 100644
--- a/src/accelerate/test_utils/scripts/test_script.py
+++ b/src/accelerate/test_utils/scripts/test_script.py
@@ -544,15 +544,15 @@ def test_split_between_processes_tensor():
 def test_breakpoint():
     accelerator = Accelerator()
     # should start with being false
-    assert accelerator.check_breakpoint() is False
+    assert accelerator.set_trigger() is False
 
     # set a breakpoint on the main process
     if accelerator.is_main_process:
-        accelerator.set_breakpoint()
+        accelerator.set_trigger()
 
     # check it's been activated across all processes
     # calls `all_reduce` and triggers a sync
-    assert accelerator.check_breakpoint() is True
+    assert accelerator.check_trigger() is True
 
 
 def main():