diff --git a/example_trainer/grpo.py b/example_trainer/grpo.py
index fe7119d5..9dc6b23f 100644
--- a/example_trainer/grpo.py
+++ b/example_trainer/grpo.py
@@ -631,10 +631,23 @@ def _attach_to_vllm_shared_tensors(
     if buffer_count > 0:
         print(f"[Setup] ✓ Moved {buffer_count} buffers to {device}")
     
-    # Also move any remaining CPU parameters (shouldn't be many)
+    # Check for unmatched parameters still on CPU
+    # These indicate a name mapping issue - don't try to move large ones
+    unmatched_cpu = []
     for name, param in model.named_parameters():
         if param.device.type == 'cpu':
-            param.data = param.to(device)
+            unmatched_cpu.append((name, param.numel()))
+    
+    if unmatched_cpu:
+        total_unmatched = sum(n for _, n in unmatched_cpu)
+        print(f"[Setup] WARNING: {len(unmatched_cpu)} parameters ({total_unmatched:,} elements) not matched!")
+        print(f"[Setup] First few unmatched: {[n for n, _ in unmatched_cpu[:5]]}")
+        print(f"[Setup] This may cause issues - check name mapping between vLLM and HuggingFace")
+        
+        # Only move small parameters (< 1M elements) - skip large unmatched ones
+        for name, param in model.named_parameters():
+            if param.device.type == 'cpu' and param.numel() < 1_000_000:
+                param.data = param.to(device)
     
     return model
 
@@ -649,6 +662,14 @@ def _create_vllm_to_hf_mapping(model: torch.nn.Module, ipc_handles: dict) -> dic
     hf_params = set(model.state_dict().keys())
     vllm_params = set(ipc_handles.keys())
     
+    print(f"[Setup] HF model has {len(hf_params)} params, vLLM exported {len(vllm_params)} tensors")
+    
+    # Debug: show sample names
+    hf_sample = sorted(list(hf_params))[:3]
+    vllm_sample = sorted(list(vllm_params))[:3]
+    print(f"[Setup] Sample HF names: {hf_sample}")
+    print(f"[Setup] Sample vLLM names: {vllm_sample}")
+    
     mapping = {}
     
     for hf_name in hf_params:
@@ -669,6 +690,20 @@ def _create_vllm_to_hf_mapping(model: torch.nn.Module, ipc_handles: dict) -> dic
             vllm_name = hf_name[6:]
             if vllm_name in vllm_params:
                 mapping[hf_name] = vllm_name
+                continue
+        
+        # For lm_head, check if it's tied to embed_tokens
+        if hf_name == "lm_head.weight" and "model.embed_tokens.weight" in vllm_params:
+            mapping[hf_name] = "model.embed_tokens.weight"
+            continue
+    
+    print(f"[Setup] Mapped {len(mapping)} HF params to vLLM tensors")
+    
+    # Show what's NOT mapped
+    unmapped = hf_params - set(mapping.keys())
+    if unmapped:
+        unmapped_sample = sorted(list(unmapped))[:5]
+        print(f"[Setup] Unmapped HF params ({len(unmapped)} total): {unmapped_sample}...")
     
     return mapping