diff --git a/example_trainer/training.py b/example_trainer/training.py
index 4f1b8ccc..1a49364f 100644
--- a/example_trainer/training.py
+++ b/example_trainer/training.py
@@ -286,27 +286,12 @@ def compute_logprob_alignment(
         stats["logprobs/training_mean"] = train_flat.mean().item()
         stats["logprobs/training_std"] = train_flat.std().item()
     
-    # Compute diff (key metric for alignment validation)
+    # Compute diff (for tracking, not validation)
+    # NOTE: Per-token comparison is NOT reliable here because inference and training
+    # logprobs come from different batch orderings and can't be aligned token-by-token.
+    # The real-time test at startup is the proper alignment validation.
     if "logprobs/inference_mean" in stats and "logprobs/training_mean" in stats:
-        # Old metric: difference of means (can be misleading)
-        stats["logprobs/diff_of_means"] = stats["logprobs/inference_mean"] - stats["logprobs/training_mean"]
-        
-        # Better metric: mean of per-token absolute differences (like real-time test)
-        # This requires matching token counts
-        min_len = min(len(inf_filtered), train_flat.numel())
-        if min_len > 0:
-            inf_subset = inf_filtered[:min_len]
-            train_subset = train_flat[:min_len].float().cpu().numpy()
-            per_token_diff = np.abs(inf_subset - train_subset)
-            stats["logprobs/mean_abs_diff"] = float(np.mean(per_token_diff))
-            stats["logprobs/diff"] = stats["logprobs/mean_abs_diff"]  # Use this as primary metric
-            
-            if debug:
-                print(f"    [DEBUG] Per-token comparison ({min_len} tokens)")
-                print(f"    [DEBUG] Mean abs diff: {stats['logprobs/mean_abs_diff']:.4f}")
-                print(f"    [DEBUG] First 5 diffs: {per_token_diff[:5]}")
-        else:
-            stats["logprobs/diff"] = stats["logprobs/diff_of_means"]
+        stats["logprobs/diff"] = stats["logprobs/inference_mean"] - stats["logprobs/training_mean"]
     
     return stats
 
@@ -404,9 +389,12 @@ def run_training_step(
     }
     
     # Compute logprob alignment stats
+    # NOTE: This comparison is approximate - inference and training logprobs
+    # come from different batching, so token-by-token alignment isn't possible.
+    # The real-time test at startup is the reliable alignment check.
     if inference_logprobs is not None and all_training_logprobs:
         alignment_stats = compute_logprob_alignment(
-            inference_logprobs, all_training_logprobs, debug=True  # Enable for debugging
+            inference_logprobs, all_training_logprobs, debug=False
         )
         _logprob_alignment_stats.update(alignment_stats)
         result["logprob_alignment"] = alignment_stats
@@ -477,36 +465,10 @@ def log_metrics(
             # - training_logprobs: from trainer's current forward pass
             # After training starts, weights change, making comparison invalid.
             # 
-            # The diff WILL increase over training - this is EXPECTED, not a bug!
-            # Trust weight sharing if:
-            # 1. --enforce-eager is set on vLLM
-            # 2. IPC attachment succeeded with ~100% coverage
-            # 3. Initial step 1 diff is < 0.5 (before much training)
-            
-            # Use mean_abs_diff if available (better metric), otherwise diff_of_means
-            mean_abs_diff = _logprob_alignment_stats.get("logprobs/mean_abs_diff")
-            
-            if mean_abs_diff is not None:
-                # Per-token comparison (like real-time test)
-                if mean_abs_diff < 0.05:
-                    status = "PERFECT"
-                elif mean_abs_diff < 0.15:
-                    status = "OK"
-                elif mean_abs_diff < 0.3:
-                    status = "OK (some drift)"
-                else:
-                    status = "stale data"
-                print(f"    LogProb Alignment: mean_abs_diff={mean_abs_diff:.4f} [{status}]")
-            else:
-                # Fallback to diff of means
-                if abs(diff) < 0.3:
-                    status = "OK"
-                elif abs(diff) < 0.5:
-                    status = "OK (data may be stale)"
-                else:
-                    status = "stale data"
-                print(f"    LogProb Alignment: inf={inf_mean:.4f}, train={train_mean:.4f}, "
-                      f"diff={diff:.4f} [{status}]")
+            # NOTE: This diff is just for monitoring, not validation!
+            # The real-time test at startup is the reliable alignment check.
+            # This diff will naturally drift as training progresses (expected).
+            print(f"    LogProb Stats: inf_mean={inf_mean:.4f}, train_mean={train_mean:.4f}")
 
     if use_wandb:
         log_dict = {