diff --git a/areal/trainer/ppo/actor.py b/areal/trainer/ppo/actor.py
index 07944a31a5..431430d71e 100644
--- a/areal/trainer/ppo/actor.py
+++ b/areal/trainer/ppo/actor.py
@@ -148,6 +148,9 @@ def _compute_advantages(self, data: dict[str, Any]) -> dict[str, Any]:
         batch_indices = torch.arange(
             bs, device=data["input_ids"].device, dtype=torch.long
         )
+        attn_mask = data["attention_mask"]
+        seqlens = attn_mask.sum(-1).long()
+        seq_no_eos_mask = seqlens == max_seqlen
 
         # Reward Penalty on length
         if self.config.overlong_reward_penalty:
@@ -170,7 +173,10 @@ def _compute_advantages(self, data: dict[str, Any]) -> dict[str, Any]:
             reward_score, max=self.reward_clip, min=-self.reward_clip
         )
         if self.reward_norm:
-            reward_score = self.reward_norm(reward_score)
+            reward_norm_mask = None
+            if self.mask_no_eos_with_zero:
+                reward_norm_mask = (~seq_no_eos_mask).to(dtype=reward_score.dtype)
+            reward_score = self.reward_norm(reward_score, reward_norm_mask)
 
         loss_mask = data["loss_mask"].float()
         loss_mask = torch.roll(loss_mask, shifts=-1, dims=-1)
@@ -196,9 +202,6 @@ def _compute_advantages(self, data: dict[str, Any]) -> dict[str, Any]:
         old_logp *= loss_mask
 
         # Compute KL-regularized rewards.
-        attn_mask = data["attention_mask"]
-        seqlens = attn_mask.sum(-1).long()
-        seq_no_eos_mask = seqlens == attn_mask.shape[1]
         rewards = -self.kl_ctl * self.kl_estimator(old_logp, ref_logp)
         kl_rewards = rewards.clone()
         # KL rewards at the next token after eos is zero.
diff --git a/tests/test_ppo_actor_reward_norm_no_eos.py b/tests/test_ppo_actor_reward_norm_no_eos.py
new file mode 100644
index 0000000000..5bb7eaff32
--- /dev/null
+++ b/tests/test_ppo_actor_reward_norm_no_eos.py
@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import importlib.machinery
+import sys
+import types
+from types import SimpleNamespace
+
+import torch
+
+
+def _install_import_shims() -> None:
+    try:
+        import torch.distributed.checkpoint.staging as staging
+
+        staging.DefaultStager = getattr(
+            staging, "DefaultStager", type("DefaultStager", (), {})
+        )
+        staging.StagingOptions = getattr(
+            staging, "StagingOptions", type("StagingOptions", (), {})
+        )
+    except Exception:
+        pass
+
+    try:
+        import torch.distributed.checkpoint.state_dict_saver as saver
+
+        saver.AsyncSaveResponse = getattr(
+            saver, "AsyncSaveResponse", type("AsyncSaveResponse", (), {})
+        )
+    except Exception:
+        pass
+
+    for name in ("swanlab", "trackio", "tabulate"):
+        if name not in sys.modules:
+            module = types.ModuleType(name)
+            module.__spec__ = importlib.machinery.ModuleSpec(name, None)
+            if name == "tabulate":
+                module.tabulate = lambda *args, **kwargs: ""
+            sys.modules[name] = module
+
+    if "tensorboardX" not in sys.modules:
+        module = types.ModuleType("tensorboardX")
+        module.__spec__ = importlib.machinery.ModuleSpec("tensorboardX", None)
+        module.SummaryWriter = type(
+            "SummaryWriter", (), {"__init__": lambda self, *a, **k: None}
+        )
+        sys.modules["tensorboardX"] = module
+
+
+_install_import_shims()
+
+from areal.api.cli_args import NormConfig  # noqa: E402
+from areal.trainer.ppo.actor import PPOActor  # noqa: E402
+from areal.utils.data import KLEstimator, Normalization  # noqa: E402
+
+
+def _make_actor() -> PPOActor:
+    actor = PPOActor.__new__(PPOActor)
+    actor.config = SimpleNamespace(
+        overlong_reward_penalty=False,
+        use_decoupled_loss=False,
+        recompute_logprob=False,
+        mask_no_eos_with_zero=True,
+    )
+    actor.reward_bias = 0.0
+    actor.reward_scaling = 1.0
+    actor.reward_clip = 1000.0
+    actor.reward_norm = Normalization(NormConfig(mean_level="batch", std_level=None))
+    actor.adv_norm = None
+    actor.kl_ctl = 0.0
+    actor.kl_estimator = KLEstimator("k1")
+    actor.discount = 1.0
+    actor.gae_lambda = 1.0
+    actor.mask_no_eos_with_zero = True
+    return actor
+
+
+def _run_case(actor: PPOActor, rewards: list[float]) -> dict[str, torch.Tensor]:
+    data = {
+        "input_ids": torch.tensor([[1, 2, 0], [1, 2, 3]]),
+        "attention_mask": torch.tensor([[1, 1, 0], [1, 1, 1]], dtype=torch.long),
+        "loss_mask": torch.tensor([[0, 1, 0], [0, 1, 1]], dtype=torch.bool),
+        "rewards": torch.tensor(rewards, dtype=torch.float32),
+        "logprobs": torch.zeros(2, 3),
+    }
+    return actor._compute_advantages(
+        {key: value.clone() for key, value in data.items()}
+    )
+
+
+def test_reward_norm_excludes_no_eos_rows_masked_from_task_reward() -> None:
+    actor = _make_actor()
+
+    baseline = _run_case(actor, [1.0, 1.0])
+    with_no_eos_outlier = _run_case(actor, [1.0, 100.0])
+
+    torch.testing.assert_close(
+        with_no_eos_outlier["tot_rewards"][0], baseline["tot_rewards"][0]
+    )
+    torch.testing.assert_close(
+        with_no_eos_outlier["advantages"][0], baseline["advantages"][0]
+    )
+    torch.testing.assert_close(
+        with_no_eos_outlier["tot_rewards"][1],
+        torch.zeros_like(with_no_eos_outlier["tot_rewards"][1]),
+    )