diff --git a/src/brevitas_examples/common/learned_round/learned_round_optimizer.py b/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
index e03bfa9f1..3fe864a4d 100644
--- a/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
+++ b/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
@@ -690,14 +690,8 @@ def apply_learned_round(
 
     def skip_full_execution(self, block, next_block, floating_point_datasets, block_forward, cache):
 
-        # We need to propagate two datasets, one is a floating point dataset to compute float out
-        # The second is a quantized dataset to create the quantized input of the next blocks
-
-        # First, we disable quantization
-        disable_quant_class = DisableEnableQuantization()
-        disable_quant_class.disable_act_quantization(block, False)
-        disable_quant_class.disable_param_quantization(block, False)
-        return_quant_tensor_state = disable_return_quant_tensor(block)
+        # We need to compute two inputs, one is a floating point one to compute float out
+        # The second is a quantized one to create the quantized input of the next blocks
 
         # If we don't have a floating_point_dataset, we retrieve it from the cache
         # The idea is that the cache contains the input to the very first block, and there is nothing
@@ -737,11 +731,6 @@ def skip_full_execution(self, block, next_block, floating_point_datasets, block_
 
         cache['output'] = tmp_cache['output']
 
-        # Re-enable quantization
-        disable_quant_class.enable_act_quantization(block, False)
-        disable_quant_class.enable_param_quantization(block, False)
-        restore_return_quant_tensor(block, return_quant_tensor_state)
-
         # Finally (!), we compute the quantized input of the next block
         block.eval()
         block.cuda()
diff --git a/src/brevitas_examples/llm/README.md b/src/brevitas_examples/llm/README.md
index a97ad548b..64c82c80a 100644
--- a/src/brevitas_examples/llm/README.md
+++ b/src/brevitas_examples/llm/README.md
@@ -54,6 +54,7 @@ usage: main.py [-h] [--model MODEL] [--seed SEED] [--nsamples NSAMPLES]
                [--export-prefix EXPORT_PREFIX]
                [--checkpoint-name CHECKPOINT_NAME] [--fuse-sequences]
                [--learned-round {None,linear_round}]
+               [--learned-round-fast-update]
 
 options:
   -h, --help            show this help message and exit
@@ -196,5 +197,8 @@ options:
   --learned-round {None,linear_round}
                         Whether to use learned round. If `None`, RTN is used
                         (default: None)
+  --learned-round-fast-update
+                        Whether to use fast update with learned round.
+                        Prototype (default: False)
 
 ```
diff --git a/src/brevitas_examples/llm/main.py b/src/brevitas_examples/llm/main.py
index 203a86e89..c8c76d4a1 100644
--- a/src/brevitas_examples/llm/main.py
+++ b/src/brevitas_examples/llm/main.py
@@ -709,7 +709,7 @@ def parse_args(args):
     parser.add_argument(
         '--learned-round-fast-update',
         default=False,
-        type=bool,
+        action="store_true",
         help='Whether to use fast update with learned round. Prototype (default: %(default)s)')
     return parser.parse_args(args)