diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
index f616f24c7..a9c6b2e73 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
@@ -265,8 +265,8 @@ class NeutralGradient(Optimizer):
                         scalar_exp_avg_sq = state["scalar_exp_avg_sq"]
                         grad_sq = (grad**2).mean()
                         conditioned_grad_sq = (conditioned_grad**2).mean()
-                        assert grad_sq - grad_sq == 0
-                        assert conditioned_grad_sq - conditioned_grad_sq == 0
+                        #assert grad_sq - grad_sq == 0
+                        #assert conditioned_grad_sq - conditioned_grad_sq == 0
                         scalar_exp_avg_sq.mul_(beta2).add_(grad_sq, alpha=(1-beta2))
                         bias_correction2 = 1 - beta2 ** (step + 1)
                         avg_grad_sq = scalar_exp_avg_sq / bias_correction2
@@ -285,7 +285,7 @@ class NeutralGradient(Optimizer):
                     delta.add_(this_delta, alpha=alpha)
                 if random.random() < 0.005:
                     print(f"Delta rms = {(delta**2).mean().item()}, shape = {delta.shape}")
-                assert delta.abs().max() < 10.0
+                #assert delta.abs().max() < 10.0
                 p.add_(delta)
                 if step % 10 == 0:
                     p.clamp_(min=-param_max, max=param_max)