diff --git a/skyrl/backends/skyrl_train_backend.py b/skyrl/backends/skyrl_train_backend.py
index 0322c94bc7..989072e5fd 100644
--- a/skyrl/backends/skyrl_train_backend.py
+++ b/skyrl/backends/skyrl_train_backend.py
@@ -1112,6 +1112,22 @@ def _validate_model_state(self, model_id: str) -> None:
         if self._dispatch is None:
             raise RuntimeError("Model not initialized")
 
+    def _staging_root(self, reference_path) -> str:
+        """Return a directory for checkpoint staging on the same filesystem as
+        ``reference_path``.
+
+        Tar archives are written/read in the engine process, but the actual
+        model files are produced/consumed by remote Ray worker actors that may
+        run on a different node.  Staging on local /tmp (``tempfile``'s default)
+        therefore breaks on multi-node deployments because the worker and the
+        engine do not share that path.  ``reference_path`` lives under
+        ``checkpoints_base`` (expected to be shared storage), so staging next to
+        it keeps the directory visible to both processes.
+        """
+        staging_root = str(os.path.dirname(reference_path))
+        os.makedirs(staging_root, exist_ok=True)
+        return staging_root
+
     def _create_tar_from_directory(self, source_dir: str, output_path: str) -> None:
         """Create an uncompressed tar archive from a directory."""
         # Ensure parent directory exists
@@ -1126,8 +1142,10 @@ def save_checkpoint(self, output_path, model_id: str) -> None:
         self._validate_model_state(model_id)
         role = self._get_role(model_id)
 
-        # Create temp directory for checkpoint
-        with tempfile.TemporaryDirectory() as temp_dir:
+        # Create temp directory for checkpoint on the same (shared) filesystem
+        # as output_path so the remote worker that writes the files and the
+        # engine that tars them both see the same path.
+        with tempfile.TemporaryDirectory(dir=self._staging_root(output_path)) as temp_dir:
             ckpt_dir = os.path.join(temp_dir, "checkpoint")
 
             # Save checkpoint directory (includes optimizer state automatically)
@@ -1143,8 +1161,10 @@ def load_checkpoint(self, checkpoint_path, model_id: str) -> None:
         self._validate_model_state(model_id)
         role = self._get_role(model_id)
 
-        # Extract tar to temp directory (filter='data' prevents path traversal attacks)
-        with tempfile.TemporaryDirectory() as temp_dir:
+        # Extract tar to temp directory on the same (shared) filesystem as
+        # checkpoint_path so the remote worker that loads the files can see it.
+        # (filter='data' prevents path traversal attacks)
+        with tempfile.TemporaryDirectory(dir=self._staging_root(checkpoint_path)) as temp_dir:
             with tarfile.open(checkpoint_path, "r") as tar:
                 tar.extractall(temp_dir, filter="data")
 
@@ -1182,7 +1202,10 @@ def save_sampler_checkpoint(self, output_path, model_id: str, persist: bool = Tr
 
         if persist:
             # TODO(tyler): For LoRA, only save the adapters instead of the full merged model
-            with tempfile.TemporaryDirectory() as temp_dir:
+            # Stage on the same (shared) filesystem as output_path so the remote
+            # worker that exports the HF model and the engine that tars it agree
+            # on the path (they may run on different nodes).
+            with tempfile.TemporaryDirectory(dir=self._staging_root(output_path)) as temp_dir:
                 hf_dir = os.path.join(temp_dir, "model")
                 self._dispatch.save_hf_model(model="policy", export_dir=hf_dir, tokenizer=self._tokenizer)
                 self._create_tar_from_directory(hf_dir, output_path)