diff --git a/slime/backends/megatron_utils/model.py b/slime/backends/megatron_utils/model.py
index 1ad6cd7957..bf6bd676f2 100644
--- a/slime/backends/megatron_utils/model.py
+++ b/slime/backends/megatron_utils/model.py
@@ -854,15 +854,15 @@ def train(
                     torch.distributed.all_reduce(values, group=tracker.get("reduce_group"))
                 if tracker.get("avg_group") is not None:
                     torch.distributed.all_reduce(values, group=tracker["avg_group"], op=torch.distributed.ReduceOp.AVG)
-                # here we assume only one mtp layer
-                mtp_losses = (tracker["values"] * mtp_loss_scale).item()
+                # Multi-head MTP: tracker["values"] is [num_mtp_layers]; aggregate below.
+                mtp_losses = tracker["values"] * mtp_loss_scale
                 MTPLossLoggingHelper.clean_loss_in_tracker()
 
                 # CI check: verify MTP loss is within expected bounds
                 if args.ci_test:
                     from slime.backends.megatron_utils.ci_utils import check_mtp_loss
 
-                    check_mtp_loss(mtp_losses)
+                    check_mtp_loss(mtp_losses.sum().item())
 
         # per train step log.
         if (
@@ -879,7 +879,9 @@ def train(
             }
             log_dict[f"train/{role_tag}grad_norm"] = grad_norm
             if args.enable_mtp_training:
-                log_dict[f"train/{role_tag}mtp_loss"] = mtp_losses
+                for _i in range(mtp_losses.shape[0]):
+                    log_dict[f"train/{role_tag}mtp_{_i + 1}_loss"] = mtp_losses[_i].item()
+                log_dict[f"train/{role_tag}mtp_loss"] = mtp_losses.sum().item()
 
             for param_group_id, param_group in enumerate(optimizer.param_groups):
                 log_dict[f"train/{role_tag}lr-pg_{param_group_id}"] = opt_param_scheduler.get_lr(param_group)