diff --git a/apps/grpo/main.py b/apps/grpo/main.py
index 93d9fb2f7..ea37c5351 100644
--- a/apps/grpo/main.py
+++ b/apps/grpo/main.py
@@ -117,7 +117,12 @@ def collate(
     return inputs, targets
 
 
-# Note: This is also available in losses.grpo_loss via `SimpleGRPOLoss`
+# TODO (T245547773): Consolidate with SimpleGRPOLoss in losses/grpo_loss.py
+# Currently duplicated because of function signature differences:
+# - This function takes logits + response, computes logprobs internally
+# - SimpleGRPOLoss takes pre-computed logprobs
+# - TitanTrainer passes logits, so would need wrapper or signature change
+# Consider refactoring TitanTrainer's loss interface to standardize this.
 def simple_grpo_loss(
     logits: torch.Tensor,
     response: torch.Tensor,
@@ -129,11 +134,30 @@ def simple_grpo_loss(
     logprobs: torch.Tensor = compute_logprobs(logits, response)
     kl = torch.exp(ref_logprobs - logprobs) - (ref_logprobs - logprobs) - 1
     per_token_policy_loss = torch.exp(logprobs - logprobs.detach()) * advantages
-    per_token_loss = -(per_token_policy_loss - beta * kl)
-    loss = (
-        ((per_token_loss * padding_mask).sum(dim=1))
+
+    # Compute mean KL per valid token
+    mean_kl = (
+        ((kl * padding_mask).sum(dim=1)) / (padding_mask.sum(dim=1).clamp(min=1.0))
+    ).mean()
+
+    # Compute mean policy loss per valid token
+    mean_policy_loss = (
+        ((per_token_policy_loss * padding_mask).sum(dim=1))
         / (padding_mask.sum(dim=1).clamp(min=1.0))
     ).mean()
+
+    # Compute loss using the means (mathematically equivalent)
+    loss = -(mean_policy_loss - beta * mean_kl)
+
+    # Log metrics
+    record_metric("grpo_loss/kl_divergence_mean", mean_kl.item(), Reduce.MEAN)
+    record_metric(
+        "grpo_loss/kl_divergence_max", (kl * padding_mask).max().item(), Reduce.MAX
+    )
+    record_metric("grpo_loss/policy_loss", mean_policy_loss.item(), Reduce.MEAN)
+    record_metric("grpo_loss/advantage_mean", advantages.mean().item(), Reduce.MEAN)
+    record_metric("grpo_loss/advantage_std", advantages.std().item(), Reduce.MEAN)
+
     return loss