diff --git a/README.md b/README.md
index b3562ac..a87f3c1 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,7 @@ from transformer_lm_gan import (
 )
 
 gan = GAN(
+    strategy = 'gumbel_one_hot', # or 'rotate' for rotation trick, may try combination of two if both fails in experiments
     generator = dict(
         num_tokens = 256,
         dim = 512,
diff --git a/transformer_lm_gan/transformer_lm_gan.py b/transformer_lm_gan/transformer_lm_gan.py
index a665c03..322c0ea 100644
--- a/transformer_lm_gan/transformer_lm_gan.py
+++ b/transformer_lm_gan/transformer_lm_gan.py
@@ -216,7 +216,7 @@ def generate(
         if not return_with_prompt:
             out = out[..., prompt_seq_len:]
 
-        return out, (filter_fn, filter_thres, temperature, eps, stack(gumbel_noises))
+        return out, (filter_fn, filter_thres, temperature, eps, stack(gumbel_noises, dim = -2))
 
     def forward(
         self,
@@ -321,8 +321,25 @@ def generate_forward(
             embed = cat((prompt_embed, embed[:, (prompt_len - 1):]), dim = -2)
 
         elif self.strategy == 'gumbel_one_hot':
+
             logits = self.generator(generated)
-            raise NotImplementedError
+            logits = logits[:, (prompt_len - 1):-1]
+
+            filter_fn, filter_thres, temperature, eps, gumbel_noises = sampling_hparams
+
+            filtered_logits = filter_fn(logits, thres = filter_thres)
+            filtered_logits = filtered_logits / max(temperature, eps)
+
+            noised_filtered_logits = filtered_logits + gumbel_noises
+
+            # do a classic gumble one-hot straight through
+
+            soft_prob = noised_filtered_logits.softmax(dim = -1)
+            soft_one_hot = soft_prob + soft_prob.detach() + F.one_hot(soft_prob.argmax(dim = -1), soft_prob.shape[-1])
+
+            embed = einsum(soft_one_hot, self.token_emb.emb.weight, 'b n e, e d -> b n d')
+
+            embed = cat((prompt_embed, embed), dim = -2)
 
         else:
             raise ValueError(f'unknown strategy')
@@ -359,12 +376,11 @@ def discriminate_forward(
             fake_next_embeds = self.token_emb(fake[:, 1:])
             fake_embed = rotate_to(fake_embed[:, :-1], fake_next_embeds)
 
-            # should not learn on the prompt portion
-
             fake_embed = cat((prompt_embed, fake_embed[:, (prompt_len - 1):]), dim = -2)
 
         elif self.strategy == 'gumbel_one_hot':
             logits = self.generator(fake)
+            logits = logits[:, (prompt_len - 1):-1]
 
             filter_fn, filter_thres, temperature, eps, gumbel_noises = sampling_hparams
 
@@ -378,8 +394,9 @@ def discriminate_forward(
             soft_prob = noised_filtered_logits.softmax(dim = -1)
             soft_one_hot = soft_prob + soft_prob.detach() + F.one_hot(soft_prob.argmax(dim = -1), soft_prob.shape[-1])
 
-            raise NotImplementedError
+            fake_embed = einsum(soft_one_hot, self.token_emb.emb.weight, 'b n e, e d -> b n d')
 
+            fake_embed = cat((prompt_embed, fake_embed), dim = -2)
         else:
             raise ValueError(f'unknown strategy {self.strategy}')