diff --git a/torchrl/collectors/_single.py b/torchrl/collectors/_single.py
index 8d18bafa8bf..dbb9043f67a 100644
--- a/torchrl/collectors/_single.py
+++ b/torchrl/collectors/_single.py
@@ -46,6 +46,20 @@
 from torchrl.weight_update.utils import _resolve_model
 
 
+def _cuda_sync_if_initialized():
+    """Synchronize CUDA only if it has been initialized.
+
+    This is a safe alternative to calling `torch.cuda.synchronize()` directly.
+    In forked subprocesses on machines with CUDA, calling `synchronize()` will
+    fail with "Cannot re-initialize CUDA in forked subprocess" if CUDA was
+    initialized in the parent process before fork.  By checking
+    `is_initialized()` first, we skip the sync in such cases since no CUDA
+    operations have occurred in this process.
+    """
+    if torch.cuda.is_initialized():
+        torch.cuda.synchronize()
+
+
 @accept_remote_rref_udf_invocation
 class Collector(BaseCollector):
     """Generic data collector for RL problems. Requires an environment constructor and a policy.
@@ -518,9 +532,14 @@ def _setup_devices(
     def _get_sync_fn(self, device: torch.device | None) -> Callable:
         """Get the appropriate synchronization function for a device."""
         if device is not None and device.type != "cuda":
-            # Cuda handles sync
+            # When destination is not CUDA, we may need to sync to wait for
+            # async GPU→CPU transfers to complete before proceeding.
             if torch.cuda.is_available():
-                return torch.cuda.synchronize
+                # Return a safe wrapper that only syncs if CUDA was actually
+                # initialized.  This avoids "Cannot re-initialize CUDA in forked
+                # subprocess" errors when using fork start method on GPU machines
+                # with CPU-only collectors.
+                return _cuda_sync_if_initialized
             elif torch.backends.mps.is_available() and hasattr(torch, "mps"):
                 return torch.mps.synchronize
             elif hasattr(torch, "npu") and torch.npu.is_available():