diff --git a/scripts/train_justrl_qwen.sh b/scripts/train_justrl_qwen.sh
index 0bf8cc0..e1ca392 100755
--- a/scripts/train_justrl_qwen.sh
+++ b/scripts/train_justrl_qwen.sh
@@ -211,6 +211,7 @@ set +e
   trainer.default_local_dir="${CHECKPOINT_DIR}" \
   trainer.is_plot="${IS_PLOT}" \
   "${VLLM_ENGINE_OVERRIDES[@]}" \
+  "$@" \
   2>&1 | tee -a "${TRAIN_LOG}"
 STATUS=${PIPESTATUS[0]}
 set -e
diff --git a/verl/verl/trainer/main_ppo.py b/verl/verl/trainer/main_ppo.py
index 0962321..6f76e02 100644
--- a/verl/verl/trainer/main_ppo.py
+++ b/verl/verl/trainer/main_ppo.py
@@ -31,6 +31,30 @@ from verl.utils.config import validate_config
 from verl.utils.device import is_cuda_available
 from verl.utils.import_utils import load_extern_type
 
+# [phase4-seed] Pilot-only seeding shim (Direct-OPD SFT-vs-RL policy-shift pilot).
+# The upstream PPO path has no global RNG seeding at all (no trainer.seed / algorithm.seed),
+# so the driver process and every Ray worker process start from unseeded python/numpy/torch
+# RNGs. This helper is called on the driver (run_ppo) and inside the Ray TaskRunner actor;
+# ActorRolloutRefWorker.init_model calls the equivalent block (separate process, separate
+# import of this module is not guaranteed to have run the driver's call).
+PHASE4_SEED = 42
+
+
+def _phase4_seed_everything(seed: int = PHASE4_SEED) -> None:
+    """Seed python/numpy/torch (+CUDA) RNGs in the current process."""
+    import random
+
+    import numpy as np
+    import torch
+
+    os.environ.setdefault("PYTHONHASHSEED", str(seed))
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    print(f"[phase4-seed] seeded python/numpy/torch RNGs with {seed} in pid={os.getpid()}")
+
 
 @hydra.main(config_path="config", config_name="ppo_trainer", version_base=None)
 def main(config):
@@ -52,6 +76,8 @@ def run_ppo(config, task_runner_class=None) -> None:
                 model paths, and training hyperparameters.
         task_runner_class: For recipe to change TaskRunner.
     """
+    _phase4_seed_everything()  # [phase4-seed] driver process
+
     # Check if Ray is not initialized
     if not ray.is_initialized():
         # Initialize Ray with a local cluster configuration
@@ -242,6 +268,8 @@ class TaskRunner:
             config: Training configuration object containing all parameters needed
                    for setting up and running the PPO training process.
         """
+        _phase4_seed_everything()  # [phase4-seed] TaskRunner actor process
+
         # Print the initial configuration. `resolve=True` will evaluate symbolic values.
         from pprint import pprint
 
diff --git a/verl/verl/workers/fsdp_workers.py b/verl/verl/workers/fsdp_workers.py
index de7893b..62e18d3 100644
--- a/verl/verl/workers/fsdp_workers.py
+++ b/verl/verl/workers/fsdp_workers.py
@@ -638,7 +638,9 @@ class ActorRolloutRefWorker(Worker, DistProfilerExtension):
         # 3. init trainer and rollout random states
         self.torch_random_states = get_torch_device().get_rng_state()
         gen_dp_rank = rollout_device_mesh["dp"].get_local_rank()
-        get_torch_device().manual_seed(gen_dp_rank + 1000)  # make sure all tp ranks have the same random states
+        # [phase4-seed] was `gen_dp_rank + 1000` (hardcoded upstream); pinned to the pilot seed
+        # base 42 so the rollout sampling generator is a documented function of the seed.
+        get_torch_device().manual_seed(gen_dp_rank + 42)  # make sure all tp ranks have the same random states
         self.gen_random_states = get_torch_device().get_rng_state()
         get_torch_device().set_rng_state(self.torch_random_states)
 
@@ -778,6 +780,16 @@ class ActorRolloutRefWorker(Worker, DistProfilerExtension):
 
     @register(dispatch_mode=Dispatch.ONE_TO_ALL)
     def init_model(self):
+        # [phase4-seed] Ray workers are separate processes: seed python/numpy/torch here too.
+        import random as _phase4_random
+
+        _phase4_random.seed(42)
+        np.random.seed(42)
+        torch.manual_seed(42)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(42)
+        print(f"[phase4-seed] worker rank={os.environ.get('RANK', '?')} seeded python/numpy/torch with 42")
+
         from verl.workers.actor import DataParallelPPOActor
 
         # This is used to import external_lib into the huggingface systems