diff --git a/trinity/common/config.py b/trinity/common/config.py
index 2efec5b24a..ea9f18e8b9 100644
--- a/trinity/common/config.py
+++ b/trinity/common/config.py
@@ -646,6 +646,7 @@ class TrainerConfig:
     # if None, automatically set to ceil(2 * model.max_model_len / ulysses_sequence_parallel_size)
     max_token_len_per_gpu: Optional[int] = None
     ulysses_sequence_parallel_size: int = 1  # sp size
+    fix_actor_microbatch_loss_scale: bool = False  # EXPERIMENTAL
     # TODO: extract more train-related params from underlying trainer engine
 
     save_strategy: SaveStrategy = SaveStrategy.UNRESTRICTED
diff --git a/trinity/common/verl_config.py b/trinity/common/verl_config.py
index ff017340df..49a241393a 100644
--- a/trinity/common/verl_config.py
+++ b/trinity/common/verl_config.py
@@ -136,6 +136,7 @@ class Actor:
     ppo_micro_batch_size_per_gpu: int = 1
     use_dynamic_bsz: Optional[bool] = None
     ppo_max_token_len_per_gpu: Optional[int] = None
+    fix_actor_microbatch_loss_scale: Optional[bool] = None  # EXPERIMENTAL
     grad_clip: Optional[float] = None
     ppo_epochs: int = 1
     shuffle: bool = False
@@ -427,6 +428,10 @@ def synchronize_config(self, config: Config) -> None:  # noqa: C901
             self.actor_rollout_ref.actor.ppo_max_token_len_per_gpu = (
                 config.trainer.max_token_len_per_gpu
             )
+        if self.actor_rollout_ref.actor.fix_actor_microbatch_loss_scale is None:
+            self.actor_rollout_ref.actor.fix_actor_microbatch_loss_scale = (
+                config.trainer.fix_actor_microbatch_loss_scale
+            )
         if self.actor_rollout_ref.actor.ulysses_sequence_parallel_size is None:
             self.actor_rollout_ref.actor.ulysses_sequence_parallel_size = (
                 config.trainer.ulysses_sequence_parallel_size
diff --git a/trinity/trainer/verl/dp_actor.py b/trinity/trainer/verl/dp_actor.py
index 3ca2d65cc7..5b9a5256c7 100644
--- a/trinity/trainer/verl/dp_actor.py
+++ b/trinity/trainer/verl/dp_actor.py
@@ -88,6 +88,16 @@ def update_policy(self, data: DataProto):  # noqa: C901
 
         mini_batches = data.split(self.config.ppo_mini_batch_size)
 
+        # EXPERIMENTAL: apply loss scale fix
+        loss_agg_mode = (
+            self.policy_loss_fn.loss_agg_mode
+            if hasattr(self.policy_loss_fn, "loss_agg_mode")
+            else "token-mean"
+        )
+        do_fix_actor_microbatch_loss_scale = self.config.fix_actor_microbatch_loss_scale and (
+            loss_agg_mode == "token-mean"
+        )
+
         metrics = {}
         for _ in range(self.config.ppo_epochs):
             for batch_idx, mini_batch in enumerate(mini_batches):
@@ -104,6 +114,12 @@ def update_policy(self, data: DataProto):  # noqa: C901
                     )
                     micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu)
 
+                if do_fix_actor_microbatch_loss_scale:
+                    # calculate the total number of response tokens in the minibatch
+                    mini_batch_token_num = torch.sum(
+                        mini_batch.batch["response_mask"].to(get_device_id())
+                    ).item()
+
                 self.actor_optimizer.zero_grad()
 
                 for micro_batch in micro_batches:
@@ -156,13 +172,19 @@ def update_policy(self, data: DataProto):  # noqa: C901
                     )
                     policy_loss = policy_loss + kl_loss
 
-                    if self.config.use_dynamic_bsz:
-                        # relative to the dynamic bsz
-                        loss = policy_loss * (
-                            response_mask.shape[0] / self.config.ppo_mini_batch_size
-                        )
+                    # set loss scale for the microbatch
+                    if not do_fix_actor_microbatch_loss_scale:
+                        # original implementation of microbatch loss scale
+                        if self.config.use_dynamic_bsz:
+                            loss_scale = response_mask.shape[0] / self.config.ppo_mini_batch_size
+                        else:
+                            loss_scale = 1.0 / self.gradient_accumulation
                     else:
-                        loss = policy_loss / self.gradient_accumulation
+                        # EXPERIMENTAL: fix for token-mean loss aggregation
+                        # scale microbatch loss according to the number of tokens (rather than sequences)
+                        loss_scale = torch.sum(response_mask).item() / (mini_batch_token_num + 1e-6)
+
+                    loss = policy_loss * loss_scale
                     loss.backward()
 
                     append_to_dict(metrics, micro_batch_metrics)