diff --git a/example/config.json b/example/config.json
index 779861a..d097c38 100644
--- a/example/config.json
+++ b/example/config.json
@@ -46,6 +46,9 @@
   "temporal_lr": 0.001,
   "quantile_loss_weight": 0.2,
   "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
+  "snr_weighted_loss": true,
+  "snr_gamma": 1.0,
+  "residual_stat_weight": 0.05,
   "sample_batch_size": 8,
   "sample_seq_len": 128
 }
diff --git a/example/train.py b/example/train.py
index 9e0fbad..9f178c0 100755
--- a/example/train.py
+++ b/example/train.py
@@ -73,6 +73,9 @@ DEFAULTS = {
     "temporal_lr": 1e-3,
     "quantile_loss_weight": 0.0,
     "quantile_points": [0.05, 0.25, 0.5, 0.75, 0.95],
+    "snr_weighted_loss": True,
+    "snr_gamma": 1.0,
+    "residual_stat_weight": 0.0,
 }
 
 
@@ -329,6 +332,13 @@ def main():
                 loss_cont = (loss_base * weights).mean()
             else:
                 loss_cont = loss_base.mean()
+
+            if bool(config.get("snr_weighted_loss", False)):
+                a_bar_t = alphas_cumprod[t].view(-1, 1, 1)
+                snr = a_bar_t / torch.clamp(1.0 - a_bar_t, min=1e-8)
+                gamma = float(config.get("snr_gamma", 1.0))
+                snr_weight = snr / (snr + gamma)
+                loss_cont = (loss_cont * snr_weight.mean()).mean()
             loss_disc = 0.0
             loss_disc_count = 0
             for i, logit in enumerate(logits):
@@ -360,6 +370,22 @@ def main():
                 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
+
+            stat_weight = float(config.get("residual_stat_weight", 0.0))
+            if stat_weight > 0:
+                # residual distribution matching (mean/std)
+                a_bar_t = alphas_cumprod[t].view(-1, 1, 1)
+                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_cont_resid
+                mean_real = x_real.mean(dim=(0, 1))
+                mean_gen = x_gen.mean(dim=(0, 1))
+                std_real = x_real.std(dim=(0, 1))
+                std_gen = x_gen.std(dim=(0, 1))
+                stat_loss = F.mse_loss(mean_gen, mean_real) + F.mse_loss(std_gen, std_real)
+                loss = loss + stat_weight * stat_loss
             opt.zero_grad()
             loss.backward()
             if float(config.get("grad_clip", 0.0)) > 0: