diff --git a/torchtitan/experiments/rl/unified/README.md b/torchtitan/experiments/rl/unified/README.md
index fa54a936da..d93e9a4e76 100644
--- a/torchtitan/experiments/rl/unified/README.md
+++ b/torchtitan/experiments/rl/unified/README.md
@@ -66,7 +66,7 @@ python torchtitan/experiments/rl/unified/infer.py --model torchtitan/experiments
 
 5. Run simple rl loop
 ```
-VLLM_BATCH_INVARIANT=1 VLLM_ATTENTION_BACKEND=FLASH_ATTN python3 torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
+python3 torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
 ```
 Right now we only support VLLM_COMPAT mode, which could achieve trainer and generator bitwise identical. We are working on support UNIFIED mode,
 which uses a unified model definition for trainer and generator.
diff --git a/torchtitan/experiments/rl/unified/actors/generator.py b/torchtitan/experiments/rl/unified/actors/generator.py
index d0ee5cf38f..ce6f83e643 100644
--- a/torchtitan/experiments/rl/unified/actors/generator.py
+++ b/torchtitan/experiments/rl/unified/actors/generator.py
@@ -18,6 +18,7 @@
 from torchtitan.experiments.rl.vllm_compat.simple_rl import (
     compute_grpo_advantages,
     compute_grpo_advantages_stable,
+    get_vllm_flash_attention_backend,
     math_reward_function,
     trivial_reward_function,
 )
@@ -197,6 +198,7 @@ def update_weights(self, vllm_compat_state: dict) -> None:
                 seed=42,  # Fixed seed for determinism
                 enforce_eager=True,
                 tensor_parallel_size=self.tp_size,  # Explicitly single GPU
+                attention_config={"backend": get_vllm_flash_attention_backend()},
             )
             logger.info("Created new vLLM engine")
         else:
diff --git a/torchtitan/experiments/rl/unified/simple_rl_multiprocess.py b/torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
index 087e4f1e70..3d93b8adbe 100644
--- a/torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
+++ b/torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
@@ -14,10 +14,13 @@
 The architecture mirrors monarch's grpo_actor.py but adapted for vLLM rollouts + TorchTitan training.
 
 Command to run:
-VLLM_BATCH_INVARIANT=1 VLLM_ATTENTION_BACKEND=FLASH_ATTN python3 torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
+python3 torchtitan/experiments/rl/unified/simple_rl_multiprocess.py
 """
 import asyncio
 import logging
+import os
+
+os.environ["VLLM_BATCH_INVARIANT"] = "1"
 
 import torch
 from monarch.actor import this_host
@@ -27,6 +30,7 @@
 from torchtitan.experiments.rl.unified.models.utils import ModelMode
 from torchtitan.experiments.rl.vllm_compat.simple_rl import (
     download_and_convert_model,
+    get_vllm_flash_attention_backend,
     load_gsm8k_dataset,
 )
 from vllm.model_executor.layers.batch_invariant import (
@@ -63,7 +67,7 @@ async def main():
     trainer_tp_size = 1
     generator_tp_size = 1
 
-    init_batch_invariance()
+    init_batch_invariance(get_vllm_flash_attention_backend())
     batch_invariant = vllm_is_batch_invariant()
     mode = ModelMode.VLLM_COMPAT
 
diff --git a/torchtitan/experiments/rl/vllm_compat/simple_rl.py b/torchtitan/experiments/rl/vllm_compat/simple_rl.py
index 5e1fdd486b..4a7a50455a 100644
--- a/torchtitan/experiments/rl/vllm_compat/simple_rl.py
+++ b/torchtitan/experiments/rl/vllm_compat/simple_rl.py
@@ -36,11 +36,17 @@
 
 from torchtitan.models.qwen3.model.args import Qwen3ModelArgs
 from transformers import AutoConfig, AutoTokenizer
-
 from vllm import LLM, SamplingParams
+
+from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.model_executor.layers.batch_invariant import init_batch_invariance
 
-init_batch_invariance()
+
+def get_vllm_flash_attention_backend() -> AttentionBackendEnum:
+    return AttentionBackendEnum.FLASH_ATTN
+
+
+init_batch_invariance(get_vllm_flash_attention_backend())
 
 
 class VLLMRolloutEngine: