diff --git a/rllib/algorithms/tests/test_callbacks_on_env_runner.py b/rllib/algorithms/tests/test_callbacks_on_env_runner.py
index 898717c6c2b2..6afa874509e0 100644
--- a/rllib/algorithms/tests/test_callbacks_on_env_runner.py
+++ b/rllib/algorithms/tests/test_callbacks_on_env_runner.py
@@ -179,9 +179,6 @@ def test_episode_and_sample_callbacks_batch_mode_complete_episodes(self):
 
             # Train one iteration.
             algo.train()
-            # We must have had exactly one `sample()` call on our EnvRunner.
-            if not multi_agent:
-                self.assertEqual(callback_obj.counts["sample"], 1)
             # We should have had at least one episode start.
             self.assertGreater(callback_obj.counts["start"], 0)
             # Episode starts must be exact same as episode ends (b/c we always complete
diff --git a/rllib/env/single_agent_env_runner.py b/rllib/env/single_agent_env_runner.py
index f9b20b498291..78e36143ea38 100644
--- a/rllib/env/single_agent_env_runner.py
+++ b/rllib/env/single_agent_env_runner.py
@@ -197,19 +197,17 @@ def sample(
                     explore=explore,
                     random_actions=random_actions,
                 )
-            # For complete episodes mode, sample as long as the number of timesteps
-            # done is smaller than the `train_batch_size`.
+            # For complete episodes mode, sample a single episode and
+            # leave coordination of sampling to `synchronous_parallel_sample`.
+            # TODO (simon, sven): The coordination will eventually move
+            # to `EnvRunnerGroup` in the future. So from the algorithm one
+            # would do `EnvRunnerGroup.sample()`.
             else:
-                total = 0
-                samples = []
-                while total < self.config.train_batch_size:
-                    episodes = self._sample_episodes(
-                        num_episodes=self.num_envs,
-                        explore=explore,
-                        random_actions=random_actions,
-                    )
-                    total += sum(len(e) for e in episodes)
-                    samples.extend(episodes)
+                samples = self._sample_episodes(
+                    num_episodes=1,
+                    explore=explore,
+                    random_actions=random_actions,
+                )
 
             # Make the `on_sample_end` callback.
             self._callbacks.on_sample_end(