diff --git a/i6_models/parts/conformer/mhsa_rel_pos.py b/i6_models/parts/conformer/mhsa_rel_pos.py
index 6f05dc26..821b1c10 100644
--- a/i6_models/parts/conformer/mhsa_rel_pos.py
+++ b/i6_models/parts/conformer/mhsa_rel_pos.py
@@ -163,15 +163,13 @@ def forward(self, input_tensor: torch.Tensor, sequence_mask: torch.Tensor) -> to
         k = key_seq.view(batch_dim_size, -1, self.num_heads, self.embed_dim_per_head)  # [B, T', #heads, F']
 
         if self.learnable_pos_emb:
-            pos_seq_q = torch.arange(time_dim_size, device=input_tensor.device)
-            pos_seq_k = torch.arange(time_dim_size, device=input_tensor.device)
-
-            distance_mat = pos_seq_k[None, :] - pos_seq_q[:, None]
-            distance_mat_clipped = torch.clamp(distance_mat, -self.rel_pos_clip, self.rel_pos_clip)
-
-            final_mat = distance_mat_clipped + self.rel_pos_clip
-
-            rel_pos_embeddings = self.rel_pos_embeddings[final_mat]  # [T, T', pos_emb_dim]
+            # 1D optimization: 2T-1 unique relative positions instead of T×T distance matrix.
+            # Build [-(T-1), ..., -1, 0, 1, ..., T-1] directly.
+            rel_pos = torch.arange(-(time_dim_size - 1), time_dim_size, device=input_tensor.device)
+            indices = torch.clamp(rel_pos, -self.rel_pos_clip, self.rel_pos_clip) + self.rel_pos_clip
+            rel_pos_embeddings = self.rel_pos_embeddings[indices].view(
+                1, 2 * time_dim_size - 1, self.pos_emb_dim
+            )  # [1, T+T'-1, pos_emb_dim]
         else:
             rel_pos_embeddings = (
                 self._sinusoidal_pe(
@@ -207,9 +205,7 @@ def forward(self, input_tensor: torch.Tensor, sequence_mask: torch.Tensor) -> to
             q_with_bias_v,
             rel_pos_embeddings.to(device=q_with_bias_v.device, dtype=q_with_bias_v.dtype),
         )  # [B, #heads, T, T'] or [B, #heads, T, T+T'+1]
-        if not self.learnable_pos_emb:
-            attn_bd = self._rel_shift_bhij(attn_bd, k_len=time_dim_size)  # [B, #heads, T, T']
-
+        attn_bd = self._rel_shift_bhij(attn_bd, k_len=time_dim_size)  # [B, #heads, T, T']
         # We use attn_mask to add BD matrix to attention scores.
         #
         # Inside torch's SDPA the mask is added after regular scaling, so to get correct