diff --git a/trinity/trainer/trainer.py b/trinity/trainer/trainer.py
index 1378449cf2..19aac1b571 100644
--- a/trinity/trainer/trainer.py
+++ b/trinity/trainer/trainer.py
@@ -10,6 +10,7 @@
 
 import ray
 
+from trinity.algorithm import SAMPLE_STRATEGY
 from trinity.common.config import Config
 from trinity.common.constants import RunningStatus, SyncMethod
 from trinity.utils.log import get_logger
@@ -23,6 +24,11 @@ def __init__(self, config: Config) -> None:
         self.logger = get_logger(__name__)
         self.engine = get_trainer_wrapper(config)
         self.explorer_ref = None
+        self.sample_strategy = SAMPLE_STRATEGY.get(config.algorithm.sample_strategy)(
+            buffer_config=config.buffer,
+            trainer_type=config.trainer.trainer_type,
+            **config.algorithm.sample_strategy_args,
+        )
 
     def prepare(self) -> None:
         """Prepare the trainer."""
@@ -32,7 +38,30 @@ def train(self) -> str:
         """Train the model."""
         while True:
             try:
-                train_continue = self.train_step()
+                # sample experiences for train step
+                try:
+                    batch, sample_metrics, exp_samples = self.sample_strategy.sample(
+                        self.engine.global_steps + 1,
+                    )
+                    successful_sampling = True
+                except StopIteration:
+                    print("No more data to train. Stop training.")
+                    if (
+                        self.engine.config.trainer.save_freq == 0
+                        or self.engine.global_steps % self.engine.config.trainer.save_freq != 0
+                    ):  # TODO: double-check this if-condition
+                        self.engine.logger.info(f"Saving at step {self.engine.global_steps}.")
+                        self.engine.save_checkpoint()
+                        self.engine.logger.info(f"Saved at step {self.engine.global_steps}.")
+                    successful_sampling = False
+                # TODO: get rid of self.engine.global_steps/config/logger?
+
+                # run train step
+                if successful_sampling:
+                    train_continue = self.engine.train_step(batch, sample_metrics, exp_samples)
+                else:
+                    train_continue = False
+
                 if not train_continue:
                     break
                 if self.need_sync():
@@ -43,14 +72,6 @@ def train(self) -> str:
         self.logger.info("--------------------\n> Trainer finished.\n--------------------")
         return self.config.trainer.name
 
-    def train_step(self) -> bool:
-        """Train one step.
-
-        Returns:
-            bool: Whether to continue training.
-        """
-        return self.engine.train_step()
-
     def need_sync(self) -> bool:
         """Whether to sync the model weight."""
         return self.engine.train_step_num % self.config.synchronizer.sync_interval == 0
@@ -95,7 +116,7 @@ def train_step_num(self) -> int:
         """Get the current training step number."""
 
     @abstractmethod
-    def train_step(self) -> bool:
+    def train_step(self, batch, sample_metrics, exp_samples) -> bool:
         """Training."""
 
     @abstractmethod
diff --git a/trinity/trainer/verl_trainer.py b/trinity/trainer/verl_trainer.py
index 2a8b1d0135..9d48421b43 100644
--- a/trinity/trainer/verl_trainer.py
+++ b/trinity/trainer/verl_trainer.py
@@ -31,7 +31,7 @@
 from verl.utils import hf_tokenizer
 from verl.utils.fs import copy_local_path_from_hdfs
 
-from trinity.algorithm import ADVANTAGE_FN, KL_FN, SAMPLE_STRATEGY
+from trinity.algorithm import ADVANTAGE_FN, KL_FN
 from trinity.algorithm.algorithm import ALGORITHM_TYPE, SFTAlgorithm
 from trinity.algorithm.algorithm_manager import AlgorithmManager
 from trinity.algorithm.utils import prefix_metrics
@@ -134,11 +134,11 @@ def __init__(
             self.kl_fn = KL_FN.get(self.algorithm_config.kl_penalty_fn)(
                 **self.algorithm_config.kl_penalty_fn_args
             )
-        self.sample_strategy = SAMPLE_STRATEGY.get(global_config.algorithm.sample_strategy)(
-            buffer_config=global_config.buffer,
-            trainer_type=global_config.trainer.trainer_type,
-            **global_config.algorithm.sample_strategy_args,
-        )
+        # self.sample_strategy = SAMPLE_STRATEGY.get(global_config.algorithm.sample_strategy)(
+        #     buffer_config=global_config.buffer,
+        #     trainer_type=global_config.trainer.trainer_type,
+        #     **global_config.algorithm.sample_strategy_args,
+        # )
         super().__init__(
             config,
             tokenizer,
@@ -287,22 +287,25 @@ def _create_dataloader(self, train_dataset, val_dataset, collate_fn, train_sampl
         # TODO: compute total training steps
         self.total_training_steps = self.config.trainer.total_training_steps or sys.maxsize
 
-    def train_step(self) -> bool:  # noqa C901
+    def train_step(self, batch, sample_metrics, exp_samples) -> bool:  # noqa C901
         self.logger.info(f"Training at step {self.global_steps + 1} started.")
         metrics = {}
-        try:
-            batch, sample_metrics, exp_samples = self.sample_strategy.sample(self.global_steps + 1)
-            prefix_metrics(sample_metrics, "sample", metrics)
-        except StopIteration:
-            print("No more data to train. Stop training.")
-            if (
-                self.config.trainer.save_freq == 0
-                or self.global_steps % self.config.trainer.save_freq != 0
-            ):
-                self.logger.info(f"Saving at step {self.global_steps}.")
-                self._save_checkpoint()
-                self.logger.info(f"Saved at step {self.global_steps}.")
-            return False
+        prefix_metrics(sample_metrics, "sample", metrics)
+
+        # try:
+        # batch, sample_metrics, exp_samples = self.sample_strategy.sample(self.global_steps + 1)
+        # prefix_metrics(sample_metrics, "sample", metrics)
+        # except StopIteration:
+        # print("No more data to train. Stop training.")
+        # if (
+        #     self.config.trainer.save_freq == 0
+        #     or self.global_steps % self.config.trainer.save_freq != 0
+        # ):
+        #     self.logger.info(f"Saving at step {self.global_steps}.")
+        #     self._save_checkpoint()
+        #     self.logger.info(f"Saved at step {self.global_steps}.")
+        # return False
+
         self.global_steps += 1
         self.logger.info(f"Sampling at step {self.global_steps} done.")
         timing_raw = {}