diff --git a/src/prime_rl/orchestrator/orchestrator.py b/src/prime_rl/orchestrator/orchestrator.py
index b99d2a3dc9..0f481362b5 100644
--- a/src/prime_rl/orchestrator/orchestrator.py
+++ b/src/prime_rl/orchestrator/orchestrator.py
@@ -555,6 +555,17 @@ async def main_loop(self) -> None:
             if train_batch is not None and not self.draining and not self.stopped.is_set():
                 await self.finalize_train_batch(train_batch)
 
+    async def start_draining(self) -> None:
+        """Stop train scheduling while allowing in-flight evals to finish."""
+        if self.draining:
+            return
+        self.draining = True
+        self.dispatcher.disable_train_scheduling()
+        n_cancelled = await self.dispatcher.cancel_inflight_train_rollouts()
+        get_logger().info(
+            f"Draining pipeline (cancelled {n_cancelled} in-flight train rollout(s); any in-flight evals will complete)"
+        )
+
     async def finalize_train_batch(self, batch: TrainBatch) -> None:
         """Ship one ``TrainBatch`` out to the trainer and handle the I/O
         side-effects (ckpt, save_rollouts, reference scoring, sender.send,
@@ -571,13 +582,7 @@ async def finalize_train_batch(self, batch: TrainBatch) -> None:
         self.last_batch_at = now
 
         if config.max_steps is not None and step > config.max_steps:
-            self.draining = True
-            self.dispatcher.disable_train_scheduling()
-            n_cancelled = await self.dispatcher.cancel_inflight_train_rollouts()
-            get_logger().info(
-                f"Draining pipeline (cancelled {n_cancelled} in-flight train rollout(s); "
-                f"any in-flight evals will complete)"
-            )
+            await self.start_draining()
             return
 
         if not batch.samples:
@@ -610,6 +615,8 @@ async def finalize_train_batch(self, batch: TrainBatch) -> None:
         await asyncio.to_thread(save_rollouts, records, get_trace_path(config.output_dir, step, "train", "effective"))
 
         await self.sender.send(TrainingBatch(examples=batch.samples, step=step))
+        if config.max_steps is not None and step == config.max_steps:
+            await self.start_draining()
         self.progress.step += 1
         self.update_dispatch_gate()
         # Checkpoint the step we just shipped (resume point: continue at step + 1).
diff --git a/tests/unit/orchestrator/test_orchestrator_lifecycle.py b/tests/unit/orchestrator/test_orchestrator_lifecycle.py
new file mode 100644
index 0000000000..f13a8b8ac0
--- /dev/null
+++ b/tests/unit/orchestrator/test_orchestrator_lifecycle.py
@@ -0,0 +1,65 @@
+import asyncio
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from prime_rl.orchestrator.orchestrator import Orchestrator
+from prime_rl.transport import TrainingSample
+
+
+def test_final_train_batch_starts_draining_immediately(tmp_path: Path) -> None:
+    rollout = SimpleNamespace(
+        is_trainable=True,
+        num_total_tokens=1,
+        num_input_tokens=1,
+        num_output_tokens=0,
+        group_id="group",
+        reward=1.0,
+        to_record=MagicMock(return_value={}),
+        scalar_advantage=MagicMock(return_value=1.0),
+    )
+    rollouts = MagicMock()
+    rollouts.__len__.return_value = 1
+    rollouts.__iter__.side_effect = lambda: iter([rollout])
+    rollouts.effective = rollouts
+    rollouts.rollouts = [rollout]
+    rollouts.metrics = SimpleNamespace(to_wandb=MagicMock(return_value={}))
+    rollouts.by_env.return_value = {"test": rollouts}
+    batch = SimpleNamespace(
+        rollouts=rollouts,
+        samples=[TrainingSample(token_ids=[1], mask=[True], logprobs=[0.0], temperatures=[1.0], env_name="test")],
+    )
+
+    orchestrator = Orchestrator.__new__(Orchestrator)
+    orchestrator.config = SimpleNamespace(max_steps=1, output_dir=tmp_path)
+    orchestrator.progress = SimpleNamespace(step=1, total_tokens=0, total_samples=0, total_problems=0)
+    orchestrator.last_batch_at = None
+    orchestrator.consecutive_empty_batches = 0
+    orchestrator.draining = False
+    orchestrator.dispatcher = SimpleNamespace(
+        disable_train_scheduling=MagicMock(),
+        cancel_inflight_train_rollouts=AsyncMock(return_value=3),
+    )
+    orchestrator.sender = SimpleNamespace(send=AsyncMock())
+    orchestrator.maybe_save_ckpt = AsyncMock(return_value=0.0)
+    orchestrator.update_dispatch_gate = MagicMock()
+    orchestrator.monitor = MagicMock()
+    orchestrator.wait_for_policy_time = 0.0
+    orchestrator.train_sink = SimpleNamespace(pre_filter_seen=0, reset_pre_filter_stats=MagicMock())
+    orchestrator.usage_reporter = None
+    orchestrator.heart = None
+    orchestrator.log_train_batch = MagicMock()
+    orchestrator.maybe_trigger_eval = MagicMock()
+
+    with (
+        patch("prime_rl.orchestrator.orchestrator.save_rollouts"),
+        patch("prime_rl.orchestrator.orchestrator.trim_process_memory"),
+    ):
+        asyncio.run(orchestrator.finalize_train_batch(batch))
+
+    assert orchestrator.draining
+    orchestrator.sender.send.assert_awaited_once()
+    orchestrator.dispatcher.disable_train_scheduling.assert_called_once_with()
+    orchestrator.dispatcher.cancel_inflight_train_rollouts.assert_awaited_once_with()
+    assert orchestrator.progress.step == 2
+    orchestrator.maybe_trigger_eval.assert_called_once_with(2)