diff --git a/users/zeyer/experiments/exp2024_04_23_baselines/ctc_recog_ext.py b/users/zeyer/experiments/exp2024_04_23_baselines/ctc_recog_ext.py
index 337348287..2e50bdd17 100644
--- a/users/zeyer/experiments/exp2024_04_23_baselines/ctc_recog_ext.py
+++ b/users/zeyer/experiments/exp2024_04_23_baselines/ctc_recog_ext.py
@@ -740,8 +740,6 @@ def _ctc_model_rescore(
     targets_spatial_dim: Dim,
 ):
     """RescoreDef API"""
-    targets_beam_dim  # noqa  # unused here
-
     import returnn.frontend as rf
     from returnn.tensor import Tensor, Dim
 
@@ -754,18 +752,26 @@ def _ctc_model_rescore(
 
     batch_dims = targets.remaining_dims(targets_spatial_dim)
 
-    # Note: This requires quite a lot of memory, as we broadcast the log_probs over the beam dim.
-    # We could also do a loop over the beam dim (or over chunks of the beam dim) to avoid this.
-
-    # Note: gradient does not matter (not used), thus no need use our ctc_loss_fixed_grad.
-    neg_log_prob = rf.ctc_loss(
-        logits=log_probs,
-        logits_normalized=True,
-        targets=targets,
-        input_spatial_dim=enc_spatial_dim,
-        targets_spatial_dim=targets_spatial_dim,
-        blank_index=model.blank_idx,
-    )
+    # Note: Using ctc_loss directly requires quite a lot of memory,
+    # as we would broadcast the log_probs over the beam dim.
+    # Instead, we do a loop over the beam dim to avoid this.
+    neg_log_prob_ = []
+    for beam_idx in range(targets_beam_dim.get_dim_value()):
+        targets_b = rf.gather(targets, axis=targets_beam_dim, indices=beam_idx)
+        targets_b_seq_lens = rf.gather(targets_spatial_dim.dyn_size_ext, axis=targets_beam_dim, indices=beam_idx)
+        targets_b_spatial_dim = Dim(targets_b_seq_lens, name=f"{targets_spatial_dim.name}_beam{beam_idx}")
+        targets_b, _ = rf.replace_dim(targets_b, in_dim=targets_spatial_dim, out_dim=targets_b_spatial_dim)
+        # Note: gradient does not matter (not used), thus no need use our ctc_loss_fixed_grad.
+        neg_log_prob = rf.ctc_loss(
+            logits=log_probs,
+            logits_normalized=True,
+            targets=targets_b,
+            input_spatial_dim=enc_spatial_dim,
+            targets_spatial_dim=targets_b_spatial_dim,
+            blank_index=model.blank_idx,
+        )
+        neg_log_prob_.append(neg_log_prob)
+    neg_log_prob, _ = rf.stack(neg_log_prob_, out_dim=targets_beam_dim)
     log_prob_targets_seq = -neg_log_prob
     assert log_prob_targets_seq.dims_set == set(batch_dims)
     return log_prob_targets_seq