diff --git a/shimmer/modules/losses.py b/shimmer/modules/losses.py
index b9642962..ce73c3e4 100644
--- a/shimmer/modules/losses.py
+++ b/shimmer/modules/losses.py
@@ -107,37 +107,36 @@ def _translation_loss(
     for domains, latents in latent_domains.items():
         if len(domains) < 2:
             continue
-        for domain_name_source in domains:
-            z = gw_mod.encode(
-                gw_mod.on_before_gw_encode_tr(
-                    {domain_name_source: latents[domain_name_source]}
-                )
+        for domain_name_target in domains:
+
+            domain_sources = {
+                domain: latents[domain]
+                for domain in domains
+                if domain != domain_name_target
+            }
+
+            z = gw_mod.encode(gw_mod.on_before_gw_encode_tr(domain_sources))
+            mod = domain_mods[domain_name_target]
+
+            domain_source_names = "/".join(domain_sources.keys())
+            loss_name = f"{domain_source_names}_to_{domain_name_target}"
+            if loss_name in losses.keys():
+                raise ValueError(f"{loss_name} is already computed.")
+
+            prediction = gw_mod.decode(z, domains={domain_name_target})[
+                domain_name_target
+            ]
+            loss_output = mod.compute_tr_loss(
+                prediction,
+                latents[domain_name_target],
+            )
+            losses[f"translation_{loss_name}"] = loss_output.loss
+            metrics.update(
+                {
+                    f"translation_{loss_name}_{k}": v
+                    for k, v in loss_output.metrics.items()
+                }
             )
-
-            for domain_name_target in domains:
-                if domain_name_source == domain_name_target:
-                    continue
-
-                mod = domain_mods[domain_name_target]
-
-                loss_name = f"{domain_name_source}_to_{domain_name_target}"
-                if loss_name in losses.keys():
-                    raise ValueError(f"{loss_name} is already computed.")
-
-                prediction = gw_mod.decode(z, domains={domain_name_target})[
-                    domain_name_target
-                ]
-                loss_output = mod.compute_tr_loss(
-                    prediction,
-                    latents[domain_name_target],
-                )
-                losses[f"translation_{loss_name}"] = loss_output.loss
-                metrics.update(
-                    {
-                        f"translation_{loss_name}_{k}": v
-                        for k, v in loss_output.metrics.items()
-                    }
-                )
     losses["translations"] = torch.stack(list(losses.values()), dim=0).mean()
     losses.update(metrics)
     return losses