diff --git a/examples/peft/merge_lora.py b/examples/peft/merge_lora.py
index 5cfb139b33..c91724dd3e 100644
--- a/examples/peft/merge_lora.py
+++ b/examples/peft/merge_lora.py
@@ -27,12 +27,23 @@
 
 Usage
 -----
-python merge_lora.py \
-    --lora-checkpoint path/to/finetune_ckpt \
-    --hf-model-path   path/to/hf_model \
-    --output          path/to/merged_ckpt \
-    [--pretrained path/to/base_ckpt] \
-    [--tp 1] [--pp 1] [--ep 1] [--cpu]
+CPU-only (single process, no GPU required)::
+
+    python merge_lora.py \
+        --lora-checkpoint path/to/finetune_ckpt \
+        --hf-model-path   path/to/hf_model \
+        --output          path/to/merged_ckpt \
+        [--pretrained path/to/base_ckpt] \
+        --cpu
+
+GPU with tensor/pipeline/expert parallelism::
+
+    torchrun --nproc_per_node <N> merge_lora.py \
+        --lora-checkpoint path/to/finetune_ckpt \
+        --hf-model-path   path/to/hf_model \
+        --output          path/to/merged_ckpt \
+        [--pretrained path/to/base_ckpt] \
+        [--tp 1] [--pp 1] [--ep 1]
 """
 
 from __future__ import annotations
@@ -54,7 +65,7 @@
 )
 from megatron.bridge.training.model_load_save import save_megatron_model
 from megatron.bridge.training.utils.checkpoint_utils import read_run_config
-from megatron.bridge.utils.common_utils import print_rank_0
+from megatron.bridge.utils.common_utils import print_rank_0, resolve_path
 
 
 logger = logging.getLogger(__name__)
@@ -100,7 +111,7 @@ def parse_args() -> argparse.Namespace:
 
 def _resolve_pretrained(lora_dir: Path, explicit: Optional[str]) -> Path:
     if explicit:
-        return Path(explicit).expanduser().resolve()
+        return resolve_path(explicit)
     cfg_path = lora_dir / "run_config.yaml"
     if not cfg_path.exists():
         raise FileNotFoundError("run_config.yaml not found in LoRA checkpoint and --pretrained not supplied")
@@ -108,7 +119,7 @@ def _resolve_pretrained(lora_dir: Path, explicit: Optional[str]) -> Path:
     base = cfg.get("checkpoint", {}).get("pretrained_checkpoint")
     if base is None:
         raise ValueError("pretrained_checkpoint missing in run_config.yaml; pass --pretrained")
-    return Path(base).expanduser().resolve()
+    return resolve_path(base)
 
 
 # -----------------------------------------------------------------------------
@@ -150,7 +161,11 @@ def merge_lora(
     model_provider.expert_tensor_parallel_size = 1
     model_provider.pipeline_dtype = torch.bfloat16
     if args.cpu:
-        assert args.tp == args.pp == args.ep == 1, "TP, PP, and EP must be 1 when using CPU merge"
+        if args.tp != 1 or args.pp != 1 or args.ep != 1:
+            logger.warning("TP, PP, and EP must be 1 when using CPU merge. Setting to 1.")
+            args.tp = 1
+            args.pp = 1
+            args.ep = 1
         if not torch.distributed.is_initialized():
             torch.distributed.init_process_group("gloo")
     model_provider.initialize_model_parallel(seed=0)
@@ -255,7 +270,7 @@ def main() -> None:
         level=logging.DEBUG if args.debug else logging.INFO, format="%(asctime)s %(levelname)s %(message)s"
     )
 
-    lora_dir = Path(args.lora_checkpoint).expanduser().resolve()
+    lora_dir = resolve_path(args.lora_checkpoint)
     if not lora_dir.exists():
         raise FileNotFoundError(f"LoRA checkpoint not found: {lora_dir}")
     base_dir = _resolve_pretrained(lora_dir, args.pretrained)
@@ -265,19 +280,16 @@ def main() -> None:
         merge_lora(
             base_dir=base_dir,
             lora_dir=lora_dir,
-            out_dir=Path(args.output).expanduser().resolve(),
+            out_dir=resolve_path(args.output),
             hf_model_path=args.hf_model_path,
             args=args,
         )
     except torch.cuda.OutOfMemoryError:
-        logger.error("Out of memory error during merge. Trying CPU merge...")
-        merge_lora(
-            base_dir=base_dir,
-            lora_dir=lora_dir,
-            out_dir=Path(args.output).expanduser().resolve(),
-            hf_model_path=args.hf_model_path,
-            args=args,
-        )
+        logger.warning("CUDA out of memory during merge. Please rerun this script on CPU by adding the `--cpu` flag.")
+        raise SystemExit(1)
+    finally:
+        if torch.distributed.is_initialized():
+            torch.distributed.destroy_process_group()
 
 
 if __name__ == "__main__":