diff --git a/code/evaluation/logical_error_rate.py b/code/evaluation/logical_error_rate.py
index 5c0c885..54b65e9 100644
--- a/code/evaluation/logical_error_rate.py
+++ b/code/evaluation/logical_error_rate.py
@@ -58,6 +58,15 @@
 from typing import Optional
 
 
+def _is_compiled(model: nn.Module) -> bool:
+    """True if *model* is already wrapped by torch.compile (OptimizedModule)."""
+    try:
+        from torch._dynamo.eval_frame import OptimizedModule
+        return isinstance(model, OptimizedModule)
+    except ImportError:
+        return False
+
+
 def _decode_batch(matcher, detectors, enable_correlated):
     return matcher.decode_batch(detectors, enable_correlations=enable_correlated)
 
@@ -917,9 +926,9 @@ def run_inference_and_decode_pre_decoder_memory(model, device, dist, cfg) -> dic
         if dist.rank == 0:
             print(f"[LER] channels_last_3d not applied: {e}")
 
-    _applied_compile = False
+    _applied_compile = _is_compiled(model)
     _compile_enabled = _get_env_bool("PREDECODER_TORCH_COMPILE", True)
-    if not _will_export_onnx and _compile_enabled:
+    if not _will_export_onnx and _compile_enabled and not _applied_compile:
         try:
             model = torch.compile(model, mode="default")
             _applied_compile = True
@@ -1686,10 +1695,11 @@ def compute_syndrome_density_reduction(model, device, dist, cfg) -> dict:
     res_ones_Z = torch.tensor(0, dtype=torch.int64, device=device)
 
     model.eval()
-    try:
-        model = torch.compile(model, mode="default")
-    except Exception:
-        pass
+    if not _is_compiled(model):
+        try:
+            model = torch.compile(model, mode="default")
+        except Exception:
+            pass
     try:
         model = model.to(memory_format=torch.channels_last_3d)
     except Exception:
@@ -1882,9 +1892,13 @@ def compute_syndrome_density_reduction(model, device, dist, cfg) -> dict:
                 res_ones_Z += R_Z[mask_Z].sum(dtype=torch.int64)
 
     t_end = time.perf_counter()
-    # Disable detailed printing for now
-    if False:
-        print(f"Time taken for batch_count={batch_count}: {t_end - t_start:.3f}s")
+
+    # Eagerly tear down DataLoader workers to release /dev/shm semaphores
+    # before LER spins up its own DataLoader.
+    del data_iter
+    del test_dataloader
+    import gc
+    gc.collect()
 
     # ----- All-reduce across ranks -----
     if torch.distributed.is_available() and torch.distributed.is_initialized():