diff --git a/verl/trainer/ppo/ray_trainer.py b/verl/trainer/ppo/ray_trainer.py
index 46271507934..66966d749ec 100644
--- a/verl/trainer/ppo/ray_trainer.py
+++ b/verl/trainer/ppo/ray_trainer.py
@@ -1606,6 +1606,28 @@ def fit(self):
                         with marked_timer("update_actor", timing_raw, color="red"):
                             actor_output = self._update_actor(batch)
 
+                        # Check if the ESI (Elastic Server Instance)/training plan is close to expiration.
+                        esi_close_to_expiration = should_save_ckpt_esi(
+                            max_steps_duration=self.max_steps_duration,
+                            redundant_time=self.config.trainer.esi_redundant_time,
+                        )
+                        # Check if the conditions for saving a checkpoint are met.
+                        # The conditions include a mandatory condition (1) and
+                        # one of the following optional conditions (2/3/4):
+                        # 1. The save frequency is set to a positive value.
+                        # 2. It's the last training step.
+                        # 3. The current step number is a multiple of the save frequency.
+                        # 4. The ESI(Elastic Server Instance)/training plan is close to expiration.
+                        if self.config.trainer.save_freq > 0 and (
+                            is_last_step
+                            or self.global_steps % self.config.trainer.save_freq == 0
+                            or esi_close_to_expiration
+                        ):
+                            if esi_close_to_expiration:
+                                print("Force saving checkpoint: ESI instance expiration approaching.")
+                            with marked_timer("save_checkpoint", timing_raw, color="green"):
+                                self._save_checkpoint()
+
                         # update weights from trainer to rollout
                         with marked_timer("update_weights", timing_raw, color="red"):
                             self.checkpoint_manager.update_weights()
@@ -1630,30 +1652,6 @@ def fit(self):
                             last_val_metrics = val_metrics
                     metrics.update(val_metrics)
 
-                # Check if the ESI (Elastic Server Instance)/training plan is close to expiration.
-                esi_close_to_expiration = should_save_ckpt_esi(
-                    max_steps_duration=self.max_steps_duration,
-                    redundant_time=self.config.trainer.esi_redundant_time,
-                )
-                # Check if the conditions for saving a checkpoint are met.
-                # The conditions include a mandatory condition (1) and
-                # one of the following optional conditions (2/3/4):
-                # 1. The save frequency is set to a positive value.
-                # 2. It's the last training step.
-                # 3. The current step number is a multiple of the save frequency.
-                # 4. The ESI(Elastic Server Instance)/training plan is close to expiration.
-                if self.config.trainer.save_freq > 0 and (
-                    is_last_step or self.global_steps % self.config.trainer.save_freq == 0 or esi_close_to_expiration
-                ):
-                    if esi_close_to_expiration:
-                        print("Force saving checkpoint: ESI instance expiration approaching.")
-                    with marked_timer("save_checkpoint", timing_raw, color="green"):
-                        # sleep replicas to avoid OOM during checkpoint saving
-                        self.checkpoint_manager.sleep_replicas()
-                        self._save_checkpoint()
-                        # wake replicas to avoid OOM during checkpoint saving
-                        self.checkpoint_manager.update_weights()
-
                 with marked_timer("stop_profile", timing_raw):
                     next_step_profile = (
                         self.global_steps + 1 in self.config.global_profiler.steps