diff --git a/example/config.json b/example/config.json
index 36e6bd8..adda1ac 100644
--- a/example/config.json
+++ b/example/config.json
@@ -44,6 +44,8 @@
   "temporal_dropout": 0.0,
   "temporal_epochs": 2,
   "temporal_lr": 0.001,
+  "quantile_loss_weight": 0.1,
+  "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
   "sample_batch_size": 8,
   "sample_seq_len": 128
 }
diff --git a/example/train.py b/example/train.py
index 7281991..9e0fbad 100755
--- a/example/train.py
+++ b/example/train.py
@@ -71,6 +71,8 @@ DEFAULTS = {
     "temporal_dropout": 0.0,
     "temporal_epochs": 2,
     "temporal_lr": 1e-3,
+    "quantile_loss_weight": 0.0,
+    "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
 }
 
 
@@ -340,6 +342,24 @@ def main():
 
             lam = float(config["lambda"])
             loss = lam * loss_cont + (1 - lam) * loss_disc
+
+            q_weight = float(config.get("quantile_loss_weight", 0.0))
+            if q_weight > 0:
+                q_points = config.get("quantile_points", [0.05, 0.25, 0.5, 0.75, 0.95])
+                q_tensor = torch.tensor(q_points, device=device, dtype=x_cont.dtype)
+                a_bar_t = alphas_cumprod[t].view(-1, 1, 1)
+                # Quantile loss on residual distribution
+                x_real = x_cont_resid
+                if cont_target == "x0":
+                    x_gen = eps_pred
+                else:
+                    x_gen = (x_cont_t - torch.sqrt(1.0 - a_bar_t) * eps_pred) / torch.sqrt(a_bar_t)
+                x_real = x_real.view(-1, x_real.size(-1))
+                x_gen = x_gen.view(-1, x_gen.size(-1))
+                q_real = torch.quantile(x_real, q_tensor, dim=0)
+                q_gen = torch.quantile(x_gen, q_tensor, dim=0)
+                quantile_loss = torch.mean(torch.abs(q_gen - q_real))
+                loss = loss + q_weight * quantile_loss
             opt.zero_grad()
             loss.backward()
             if float(config.get("grad_clip", 0.0)) > 0: