diff --git a/palm_rlhf_pytorch/palm_rlhf_pytorch.py b/palm_rlhf_pytorch/palm_rlhf_pytorch.py
index d35011d..4673be5 100644
--- a/palm_rlhf_pytorch/palm_rlhf_pytorch.py
+++ b/palm_rlhf_pytorch/palm_rlhf_pytorch.py
@@ -22,6 +22,9 @@
 def exists(val):
     return val is not None
 
+def default(val, d):
+    return val if exists(val) else d
+
 def identity(t, *args, **kwargs):
     return t
 
@@ -330,9 +333,9 @@ def set_dropout(self, dropout):
                 module.p = dropout
         return self
 
-    def add_finetune_params(self, scope):
+    def add_finetune_params(self, scope, lora_r = None):
         assert scope not in self.finetune_modules, f'finetune scope {scope} already found'
-        dim, dim_head, heads, r, device = self.dim, self.dim_head, self.heads, self.lora_r, self.device
+        dim, dim_head, heads, r, device = self.dim, self.dim_head, self.heads, default(lora_r, self.lora_r), self.device
 
         q_inner_dim = heads * dim_head
         kv_inner_dim = dim_head
@@ -497,7 +500,8 @@ def __init__(
         dropout = 0.1,
         num_binned_output = 0.,
         use_lora = True,
-        reward_lora_scope = 'reward'
+        lora_r = 8,
+        reward_lora_scope = 'reward',
     ):
         super().__init__()
 
@@ -507,7 +511,7 @@ def __init__(
         self.reward_lora_scope = reward_lora_scope if use_lora else None
 
         if exists(self.reward_lora_scope):
-            self.palm.add_finetune_params(reward_lora_scope)
+            self.palm.add_finetune_params(reward_lora_scope, lora_r = lora_r)
 
         dim = palm.dim
 
@@ -601,6 +605,8 @@ def __init__(
         pooled_values = False,
         actor_lora = True,
         critic_lora = True,
+        actor_lora_r = 8,
+        critic_lora_r = 8,
         actor_lora_scope = 'actor',
         critic_lora_scope = 'critic',
         actor_dropout = 0.,
@@ -624,10 +630,10 @@ def __init__(
         self.critic_lora_scope = critic_lora_scope if critic_lora else None
 
         if self.actor_lora:
-            self.actor_palm.add_finetune_params(actor_lora_scope)
+            self.actor_palm.add_finetune_params(actor_lora_scope, lora_r = actor_lora_r)
 
         if self.critic_lora:
-            self.critic_palm.add_finetune_params(critic_lora_scope)
+            self.critic_palm.add_finetune_params(critic_lora_scope, lora_r = critic_lora_r)
 
         self.pooled_values = pooled_values
         self.value_head = nn.Sequential(
diff --git a/palm_rlhf_pytorch/ppo.py b/palm_rlhf_pytorch/ppo.py
index 519ca91..712fd71 100644
--- a/palm_rlhf_pytorch/ppo.py
+++ b/palm_rlhf_pytorch/ppo.py
@@ -138,6 +138,8 @@ def __init__(
         critic_adam_eps = 1e-7,
         actor_lora = True,
         critic_lora = True,
+        actor_lora_r = 8,
+        critic_lora_r = 8,
         critic_pooled_values = True,
         actor_dropout = 0.,
         critic_dropout = 0.,
@@ -182,6 +184,8 @@ def __init__(
                 palm = palm,
                 actor_lora = actor_lora,
                 critic_lora = critic_lora,
+                actor_lora_r = actor_lora_r,
+                critic_lora_r = critic_lora_r,
                 pooled_values = critic_pooled_values,
                 actor_dropout = actor_dropout,
                 critic_dropout = critic_dropout
diff --git a/setup.py b/setup.py
index 97d96d8..ce1b5fa 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'PaLM-rlhf-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.43',
+  version = '0.0.44',
   license='MIT',
   description = 'PaLM + Reinforcement Learning with Human Feedback - Pytorch',
   author = 'Phil Wang',