diff --git a/docs/README.md b/docs/README.md
index ab1271e..2b3eb8d 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -79,6 +79,9 @@ However, while this solution boasts being lightweight, there are some caveats fo
   * `hf`-ifying it is possible, but it'd be a chore to set up the tokenizer properly
 * it still seems like the phase of the moon matters with how it wants to cooperate
   * some eval tests it seems fine, other times issues like word errors will crop up
+* the `NAR-len` requires CFGs > 2-ish to cooperate
+  * this isn't *so* much of an issue, but this can lead to user error, and CFG incurs an additional sampling step per step.
+  * guidance distillation would be nice, but distillation in general harms finetuning (assuming this just as likely harms it)
 
 
 ## Notices and Citations
diff --git a/docs/models.md b/docs/models.md
index 093d565..c11be9e 100644
--- a/docs/models.md
+++ b/docs/models.md
@@ -96,6 +96,7 @@ It is ***crucial*** to:
   * without this, you ***will*** get stuttering and unaligned utterances. I do not know why this is such a big problem but I imagine this "interleaves" many different sequences between each step.
 * use unfiltered/unprocessed logit scores:
   * not that crucial, but helps stability.
+* use a CFG strength of at least 2
 
 It is not required to train a model from scratch to use this modality, as training from existing weights works just as well, if not better (as it can piggyback off the original model).
 * additional training is still required to help confidence issues and to condition the model to not fall apart for longer durations.
@@ -336,7 +337,7 @@ A bulk of it pertains to modifying `LlamaAttention` and detecting available atte
   * `fused_attn`: uses an implementation using `triton` (tested on my 7900XTX and V100s), but seems to introduce errors when used to train after a while
   * `sageattn`: uses [SageAttention](https://github.com/thu-ml/SageAttention).
     * training under this is untested, but dropout is not applied (yet).
-  * `default`: uses the naive path for hte internal implementation (used for attention-debugging purposed)
+  * `default`: uses the naive path for the internal implementation (used for attention-debugging purposed)
 * `transformers` Llama\*Attention implementations:
   * `eager`: default `LlamaAttention`
   * `sdpa`: integrated `LlamaSdpaAttention` attention model
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 304d39a..a1e2960 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -253,6 +253,7 @@ class AR_NAR(Base):
 		max_steps = sampling_kwargs.get("max_steps", 25)
 		refine_on_stop = sampling_kwargs.get("refine_on_stop", False)
 		entropix_sampling = sampling_kwargs.get("entropix_sampling", False)
+		annealed_sampling = sampling_kwargs.get("annealed_sampling", True)
 
 		# greedy sampling is very, very much preferred, but using greedy logit scores later helps enough
 		temperature = sampling_kwargs.pop("temperature", 0.0)
@@ -261,12 +262,14 @@ class AR_NAR(Base):
 		cfg_rescale = sampling_kwargs.pop("cfg_rescale", 0.75)
 		start_noise = sampling_kwargs.get("denoise_start", 0.0)
 		end_noise = sampling_kwargs.get("denoise_end", 1.0)
+		remasking = sampling_kwargs.get("remasking", True)
 		max_steps = math.floor(max_steps * (end_noise - start_noise))
 
 		len_list = [ clamp(l, min_length, max_length) for l in len_list ]
 		
 		# force set CFG because too low / no CFG causes issues
-		cfg_strength = max( cfg_strength, 3.0 )
+		minimum_cfg_strength = sampling_kwargs.get("minimum_cfg_strength", 3.0)
+		cfg_strength = max( cfg_strength, minimum_cfg_strength )
 
 		# if we're denoising from an existing sequence
 		if start_noise > 0.0 and resps_list is not None:
@@ -306,8 +309,10 @@ class AR_NAR(Base):
 			annealing = 1.0 - timestep
 			# get noise level, per cosine scheduling
 			noise_p = math.cos( timestep * math.pi * 0.5 )
+			# proportion of tokens to remask
+			remask_p = 1.0 / max_steps if remasking else 0
 			# pick the worst scoring tokens to mask off
-			masked_indices = [ score.topk( max(int( noise_p * seq_len ), 1), dim=-1 ).indices for score, seq_len in zip(scores, len_list) ]
+			masked_indices = [ score.topk( clamp( int( noise_p * seq_len + remask_p * seq_len ), 1, seq_len), dim=-1 ).indices for score, seq_len in zip(scores, len_list) ]
 			# mask off inputs
 			resps_list = [ resp.scatter(0, indices, self.stop_token) for resp, indices in zip( resps_list, masked_indices ) ]
 			# boolean mask
@@ -315,8 +320,12 @@ class AR_NAR(Base):
 			# timestep inputs
 			time_list = [ timestep for _ in range(batch_size) ]
 
-			sampling_temperature = temperature * annealing
-			sampling_cfg = cfg_strength * timestep
+			sampling_temperature = temperature * annealing if annealed_sampling else temperature
+			sampling_cfg = cfg_strength * timestep if annealed_sampling else temperature
+
+			# avoid useless CFG sampling
+			if sampling_cfg < minimum_cfg_strength * 0.5:
+				sampling_cfg = 0
 
 			# setup inputs
 			inputs = super().inputs(