diff --git a/xtuner/v1/loss/base_loss_ctx.py b/xtuner/v1/loss/base_loss_ctx.py
index 79680ddb5..1b42bebd0 100644
--- a/xtuner/v1/loss/base_loss_ctx.py
+++ b/xtuner/v1/loss/base_loss_ctx.py
@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from abc import ABC, abstractmethod
-from typing import Annotated, Any, Literal, Self, TypeVar
+from typing import Annotated, Any, Literal, TypeVar
 
 import torch
 import torch.distributed as dist
@@ -9,6 +9,7 @@
 from pydantic import BaseModel, ConfigDict
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.nn.functional import all_reduce
+from typing_extensions import Self
 
 from xtuner.v1.loss.utils import sp_split
 
diff --git a/xtuner/v1/train/rl_trainer.py b/xtuner/v1/train/rl_trainer.py
index 5aa34ed8e..cd3f02839 100644
--- a/xtuner/v1/train/rl_trainer.py
+++ b/xtuner/v1/train/rl_trainer.py
@@ -245,6 +245,7 @@ def __init__(
 
         self._total_epochs = total_epochs
         self._cur_step = 0
+        self._global_train_step = 1
 
         if skip_checkpoint_validation:
             patch_default_save_plan()
@@ -554,24 +555,19 @@ def _train_step(self, rollout_idx: int, data_groups, multimodal_train_infos, ste
             self.logger.info(f"Prepared {len(data_batches)} training data batches")
             self._log_data_info(rollout_idx, data_info)
 
-        self._writer.add_scalar(
-            tag="time/onload",
-            scalar_value=step_timer_dict["onload"],
-            global_step=rollout_idx,
-        )
-
-        self._writer.add_scalar(
-            tag="time/prepare_data",
-            scalar_value=step_timer_dict["prepare_data"],
-            global_step=rollout_idx,
-        )
-
         with timer("training", step_timer_dict):
             workers_log_item: List[WorkerLogItem] = ray.get(
                 self._train_controller.fit.remote(
                     data_batches, pack_max_length=self._train_worker_cfg.pack_max_length, rollout_idx=rollout_idx
                 )
             )
+        self._log_train_metrics(rollout_idx, step_timer_dict, workers_log_item)
+
+    def _log_train_metrics(self, rollout_idx: int, step_timer_dict: dict, workers_log_item: List[WorkerLogItem]):
+        self._writer.add_scalar(tag="time/onload", scalar_value=step_timer_dict["onload"], global_step=rollout_idx)
+        self._writer.add_scalar(
+            tag="time/prepare_data", scalar_value=step_timer_dict["prepare_data"], global_step=rollout_idx
+        )
         self._writer.add_scalar(tag="time/training", scalar_value=step_timer_dict["training"], global_step=rollout_idx)
 
         rank0_log_item = workers_log_item[0]
@@ -591,6 +587,7 @@ def _train_step(self, rollout_idx: int, data_groups, multimodal_train_infos, ste
         tb_entropy = {"entropy/train": rank0_log_item["train_entropy"]}
         self._writer.add_scalars(tag_scalar_dict=tb_entropy, global_step=rollout_idx)
 
+        train_start_step = self._global_train_step
         for worker_idx, log_item in enumerate(workers_log_item):
             mini_batch_metrics: dict[str, List[float]] = {}
             for mini_batch_log in log_item["train_metrics"]:
@@ -599,13 +596,21 @@ def _train_step(self, rollout_idx: int, data_groups, multimodal_train_infos, ste
                 for k, v in rl_worker_log.items():
                     mini_batch_metrics.setdefault(k, []).append(cast(float, v))
 
+            for key, value in mini_batch_metrics.items():
+                avg_value = sum(value) / len(value)
+                self._writer.add_scalar(
+                    tag=f"train_metrics/worker_{worker_idx}/step_avg_{key}",
+                    scalar_value=avg_value,
+                    global_step=rollout_idx,
+                )
+
             for key, value in mini_batch_metrics.items():
                 for i, v in enumerate(value):
-                    global_step = (rollout_idx - 1) * len(value) + i + 1
+                    current_step = train_start_step + i
                     self._writer.add_scalar(
                         tag=f"train_metrics/worker_{worker_idx}/{key}",
                         scalar_value=v,
-                        global_step=global_step,
+                        global_step=current_step,
                     )
 
             rank_sft_log = log_item["sft_train_metrics"]
@@ -616,6 +621,9 @@ def _train_step(self, rollout_idx: int, data_groups, multimodal_train_infos, ste
                     global_step=rollout_idx,
                 )
 
+        num_mini_batches = len(workers_log_item[0]["train_metrics"])
+        self._global_train_step += num_mini_batches
+
     def _sync_weights_and_save(self, rollout_idx: int, step_timer_dict: dict):
         """Synchronizes weights and saves checkpoints."""
         with timer("save_ckpt", step_timer_dict):