diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index d37910b12..5396b1895 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -175,6 +175,13 @@ class Zipformer(EncoderInterface):
 
         num_encoders = len(downsampling_factor)
         for i in range(num_encoders):
+
+            # give more-downsampled layers a higher final layer_skip_rate, to discourage
+            # the model from relying too much on them at the end.  (They tend to train
+            # slowly, so don't increase their skip-rate at the beginning.)
+            layer_skip_rate = (ScheduledFloat((0.0, 0.5), (4000.0, 0.0), default=0,) +
+                               (0.025 * (downsampling_factor[i] ** 0.5)))
+
             encoder_layer = ZipformerEncoderLayer(
                 embed_dim=encoder_dim[i],
                 pos_dim=pos_dim,
@@ -185,6 +192,7 @@ class Zipformer(EncoderInterface):
                 feedforward_dim=feedforward_dim[i],
                 dropout=dropout,
                 cnn_module_kernel=cnn_module_kernel[i],
+                layer_skip_rate=layer_skip_rate,
             )
 
             # For the segment of the warmup period, we let the Conv2dSubsampling
@@ -209,7 +217,8 @@ class Zipformer(EncoderInterface):
                 )
                 # we are adding a new attribute here.
                 # this will be interpreted by get_named_parameter_groups_with_lrs().
-                encoder.lr_scale = downsampling_factor[i] ** -0.2
+                encoder.lr_scale = downsampling_factor[i] ** -0.25
+
             encoders.append(encoder)
         self.encoders = nn.ModuleList(encoders)