diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
index c7c309e21..2d1b83e33 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
@@ -812,9 +812,7 @@ class DecorrelateFunction(torch.autograd.Function):
                               # is not differentiable..
             loss = _compute_correlation_loss(cov, ctx.eps)
             assert loss.dtype == torch.float32
-            #print(f"x_sqnorm mean = {x_sqnorm.mean().item()}, x_sqnorm_mean={x_sqnorm.mean().item()}, x_desired_sqscale_sum={x_desired_sqscale.sum()}, x_grad_old_sqnorm mean = {x_grad_old_sqnorm.mean().item()}, x**2_mean = {(x**2).mean().item()}, scaled_x**2_mean = {(scaled_x**2).mean().item()}, (cov-abs-mean)={cov.abs().mean().item()}, old_cov_abs_mean={old_cov.abs().mean().item()}, loss = {loss}")
 
-            #if random.random() < 0.01:
             if random.random() < 0.05:
                 logging.info(f"Decorrelate: loss = {loss}")
 
@@ -858,7 +856,7 @@ class Decorrelate(torch.nn.Module):
                  scale: float = 0.1,
                  apply_steps: int = 1000,
                  eps: float = 1.0e-05,
-                 beta: float = 0.8,
+                 beta: float = 0.95,
                  channel_dim: int = -1):
         super(Decorrelate, self).__init__()
         self.scale = scale