diff --git a/examples/deepswe/train_deepswe_nb.py b/examples/deepswe/train_deepswe_nb.py
index e847910fd..a36e48c78 100644
--- a/examples/deepswe/train_deepswe_nb.py
+++ b/examples/deepswe/train_deepswe_nb.py
@@ -185,6 +185,15 @@
     "--loss_agg_mode", type=str, default="sequence-mean-token-scale"
 )
 parser.add_argument("--advantage_estimator", type=str, default="rloo")
+parser.add_argument(
+    "--degenerate_group_masking",
+    type=bool,
+    default=False,
+    help=(
+        "Whether to mask out groups whose advantages are all zero. "
+        "Default is False to align with rLLM DeepSWE."
+    ),
+)
 
 
 # Other
@@ -455,6 +464,7 @@
 )
 LOSS_AGG_MODE = args.loss_agg_mode
 ADVANTAGE_ESTIMATOR = args.advantage_estimator
+DEGENERATE_GROUP_MASKING = args.degenerate_group_masking
 
 
 # %%
@@ -778,6 +788,7 @@ def transform(entry):
     "filter_statuses": FILTER_STATUSES,
     "loss_agg_mode": LOSS_AGG_MODE,
     "advantage_estimator": ADVANTAGE_ESTIMATOR,
+    "degenerate_group_masking": DEGENERATE_GROUP_MASKING,
 }
 
 grpo_config = agentic_grpo_learner.GRPOConfig(**config_kwargs)
@@ -856,6 +867,7 @@ def mixed_type_batch_fn(elements):
       "filter_statuses": (
           [s.name for s in FILTER_STATUSES] if FILTER_STATUSES else None
       ),
+      "degenerate_group_masking": DEGENERATE_GROUP_MASKING,
       # Mesh topology
       "num_devices": len(devices),
       "rollout_mesh_fsdp": rollout_fsdp,