diff --git a/README.md b/README.md
index 5ed42ae..e4f0884 100755
--- a/README.md
+++ b/README.md
@@ -124,9 +124,15 @@ To synthesize speech, invoke either (if exported the models): `python -m vall_e
 
 Some additional flags you can pass are:
 * `--max-ar-steps`: maximum steps for inferencing through the AR model. Each second is 75 steps.
-* `--ar-temp`: sampling temperature to use for the AR pass. During experimentation, `0.95` provides the most consistent output.
-* `--nar-temp`: sampling temperature to use for the NAR pass. During experimentation, `0.2` provides the most clean output.
 * `--device`: device to use (default: `cuda`, examples: `cuda:0`, `cuda:1`, `cpu`)
+* `--ar-temp`: sampling temperature to use for the AR pass. During experimentation, `0.95` provides the most consistent output, but values close to it works file.
+* `--nar-temp`: sampling temperature to use for the NAR pass. During experimentation, `0.2` provides clean output, but values upward of `0.6` seems fine too.
+
+And some experimental sampling flags you can use too (your mileage will ***definitely*** vary):
+* `--top-p`: limits the sampling pool to top sum of values that equal `P`% probability in the probability distribution.
+* `--top-k`: limits the sampling pool to the top `K` values in the probability distribution.
+* `--repetition-penalty`: modifies the probability of tokens if they have appeared before. In the context of audio generation, this is a very iffy parameter to use.
+* `--length-penalty`: (AR only) modifies the probability of the stop token based on the current sequence length. This is ***very*** finnicky.
 
 ## To-Do
 
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index b40f528..9e6534c 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -411,8 +411,6 @@ class Base(nn.Module):
 			)
 			
 			return logits
-
-
 		
 		# (NAR) return the entire generated response
 		if quant_levels is not None:
@@ -427,7 +425,7 @@ class Base(nn.Module):
 		# perform repetition penalizing	
 		logits = [ reptition_penalize(logit, previous=resps[:, 0], factor=sampling_repetition_penalty) for logit, resps in zip( logits, resps_list ) ]
 
-		# perform length penalizing
+		# (AR) perform length penalizing
 		if quant_levels is None and self.causal:
 			logits = [ length_penalize(logit, length=l + 1, factor=sampling_length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, resps_list) ) ]