diff --git a/llms/mlx_lm/lora.py b/llms/mlx_lm/lora.py
index 1f684d27..1e4fe3d5 100644
--- a/llms/mlx_lm/lora.py
+++ b/llms/mlx_lm/lora.py
@@ -63,11 +63,16 @@ CONFIG_DEFAULTS = {
     "config": None,
     "grad_checkpoint": False,
     "lr_schedule": None,
+    "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
+
+    # GRPO args
     "reference_model_path": None,
     "group_size": 4,
     "beta": 0.1,
     "epsilon": 1e-4,
-    "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
+    "max_completion_length": 512,
+    "use_chat_template": False,
+    "use_prompt": False,
 }
 
 
@@ -178,9 +183,15 @@ def build_parser():
     parser.add_argument(
         "--group-size",
         type=int,
-        help="Number of responses per prompt.",
+        help="Number of generations.",
         default=4,
     )
+    parser.add_argument(
+        "--max-completion-length",
+        type=int,
+        help="Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left.",
+        default=512,
+    )
     parser.add_argument(
         "--beta",
         type=float,
@@ -193,6 +204,18 @@ def build_parser():
         help="The Epsilon for numerical stability.",
         default=1e-4,
     )
+    parser.add_argument(
+        "--use-chat-template",
+        type=bool,
+        help="If the model is a Chat model, use the Chat template.",
+        default=False,
+    )
+    parser.add_argument(
+        "--use-prompt",
+        type=bool,
+        help="Rather to use the prompt from teh R1 paper.",
+        default=False,
+    )
     return parser
 
 
@@ -262,6 +285,7 @@ def train_model(
             steps_per_save=args.save_every,
             adapter_file=adapter_file,
             max_seq_length=args.max_seq_length,
+            max_completion_length=args.max_completion_length,
             grad_checkpoint=args.grad_checkpoint,
             beta=args.beta,
             group_size=args.group_size,
@@ -273,7 +297,7 @@ def train_model(
             reference_model, _ = load(args.reference_model_path)
             reference_model = reference_model.freeze()
         else:
-            reference_model, _ = None, None
+            reference_model, _ = load(args.model)
         
         train_grpo(
             model=model,
diff --git a/llms/mlx_lm/tuner/datasets.py b/llms/mlx_lm/tuner/datasets.py
index 163b1be7..b31656c6 100644
--- a/llms/mlx_lm/tuner/datasets.py
+++ b/llms/mlx_lm/tuner/datasets.py
@@ -16,14 +16,33 @@ class GRPODataset:
         data: List[Dict[str, str]],
         tokenizer: PreTrainedTokenizer,
         prompt_key: str = "prompt",
-        answer_key: str = "answer"
+        answer_key: str = "answer",
+        use_chat_template: bool = False,
+        use_prompt: bool = False
     ):
         self._data = []
         for item in data:
             prompt_str = str(item[prompt_key])
             answer_str = str(item[answer_key])
-            prompt_tokens = tokenizer.encode(prompt_str)
-            answer_tokens = tokenizer.encode(answer_str)
+            if use_chat_template:
+                prompt_tokens = tokenizer.apply_chat_template(
+                    [
+                        {'role': 'system', 'content': """A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
+                The assistantfirst thinks about the reasoning process in the mind and then provides the user with the answer.
+                The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>."""},
+                    {'role': 'user', 'content': prompt_str}
+                    ],
+                )
+                answer_tokens = tokenizer.encode(answer_str)
+            else:
+                if use_prompt:
+                    prompt_tokens = tokenizer.encode(f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
+            The assistantfirst thinks about the reasoning process in the mind and then provides the user with the answer.
+            The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.
+            User: {prompt_str}. Assistant: """)
+                else:
+                    prompt_tokens = tokenizer.encode(prompt_str)
+                answer_tokens = tokenizer.encode(answer_str)
             self._data.append((prompt_tokens, answer_tokens, prompt_str, answer_str))
 
     def __getitem__(self, idx: int) -> Tuple[List[int], List[int], str, str]:
diff --git a/llms/mlx_lm/tuner/grpo_trainer.py b/llms/mlx_lm/tuner/grpo_trainer.py
index 29518d8f..3b5dc2e9 100644
--- a/llms/mlx_lm/tuner/grpo_trainer.py
+++ b/llms/mlx_lm/tuner/grpo_trainer.py
@@ -12,8 +12,6 @@ from mlx.utils import tree_flatten
 
 from .trainer import grad_checkpoint, TrainingArgs, TrainingCallback, average_gradients, iterate_batches
 
-from mlx_lm.utils import generate_step
-
 
 @dataclass
 class GRPOTrainingArgs(TrainingArgs):
@@ -27,6 +25,9 @@ class GRPOTrainingArgs(TrainingArgs):
     epsilon: float = field(
         default=1e-4, metadata={"help": "The Epsilon for numerical stability."}
     )
+    max_completion_length: int = field(
+        default=512, metadata={"help": "Number of Generations."}
+    )
     reference_model_path: str = field(
         default=None,
         metadata={
@@ -36,7 +37,6 @@ class GRPOTrainingArgs(TrainingArgs):
 
 
 def generate_grpo(model, prompt, max_tokens, tokenizer, temperature=1.0):
-    model.eval()
     if len(prompt.shape) == 1:
         prompt = prompt[None, :]
     
@@ -58,11 +58,7 @@ def generate_grpo(model, prompt, max_tokens, tokenizer, temperature=1.0):
         
         token_value = next_token.item()
         generated.append(next_token)
-        
-        # Clear intermediate tensors
-        del logits, token_logits, probs
-        mx.metal.clear_cache()
-        
+
         current_prompt = mx.concatenate([current_prompt, next_token[None]])
         if token_value == tokenizer.eos_token_id:
             break
@@ -72,12 +68,6 @@ def generate_grpo(model, prompt, max_tokens, tokenizer, temperature=1.0):
         
     result = mx.concatenate([prompt[0], mx.stack(generated)])
     mx.eval(result)
-    model.train()
-    
-    # Clear generated tokens
-    del generated
-    mx.metal.clear_cache()
-    
     return result
 
 
@@ -192,11 +182,6 @@ def get_per_token_logps(model, inputs, lengths):
         ).squeeze(-1)  # [seq_len]
         
         per_token_logps.append(token_log_probs)
-        
-        # Clean up intermediates
-        del seq_logits, seq_targets, log_probs, token_log_probs
-        mx.metal.clear_cache()
-    
     return per_token_logps
 
 
@@ -232,15 +217,9 @@ def grpo_loss(
                 all_completions.append(completion_ids)
                 all_completion_texts.append(completion_text)
                 
-                del completion_ids
-                mx.metal.clear_cache()
-                
             except Exception as e:
                 print(f"Generation error: {e}")
                 continue
-            
-        del prompt_tensor
-        mx.metal.clear_cache()
 
     # Prepare inputs
     expanded_answers = []
@@ -264,25 +243,11 @@ def grpo_loss(
             mask = mx.ones_like(completion_ids)
         padded_completions.append(padded_ids)
         attention_masks.append(mask)
-        
-        del completion_ids
-        if padding_length > 0:
-            del padding
-        del mask
-        mx.metal.clear_cache()
     
     inputs = mx.stack(padded_completions)
     attention_mask = mx.stack(attention_masks)
     lengths = attention_mask.sum(axis=1)
     
-    del padded_completions, attention_masks
-    mx.metal.clear_cache()
-    
-    # Get logits and compute log probabilities
-    logits = model(inputs).astype(mx.float32)
-    log_probs = nn.log_softmax(logits[:, :-1, :], axis=-1)
-    targets = inputs[:, 1:]
-    
     # Current policy probabilities
     token_log_probs = get_per_token_logps(model, inputs, lengths)
     
@@ -302,9 +267,6 @@ def grpo_loss(
         
         padded_log_probs.append(mx.concatenate([token_log_probs[i], padding]))
         padded_ref_log_probs.append(mx.concatenate([ref_token_log_probs[i], padding]))
-        
-        del padding
-        mx.metal.clear_cache()
     
     token_log_probs = mx.stack(padded_log_probs)
     ref_token_log_probs = mx.stack(padded_ref_log_probs)
@@ -360,10 +322,6 @@ def grpo_loss(
         reward_metrics[f'{func_name}_mean'] = mx.mean(func_rewards)
         reward_metrics[f'{func_name}_std'] = mx.std(func_rewards)
     
-    # Clean up
-    del all_completions
-    mx.metal.clear_cache()
-    
     metrics = {
         'total_rewards_mean': mx.mean(rewards),
         'total_rewards_std': mx.std(rewards),
@@ -440,7 +398,7 @@ def evaluate_grpo(
     group_size: int,
     max_seq_length,
     reward_funcs = None,
-    loss: callable = grpo_loss,
+    loss_fn: callable = grpo_loss,
     iterate_batches: callable = iterate_grpo_batches
 ):
     """
@@ -466,7 +424,7 @@ def evaluate_grpo(
         ),
     ):
         # Calculate loss for current batch
-        losses, toks, metrics = loss(
+        losses, toks, metrics = loss_fn(
             model=model,
             tokenizer=tokenizer,
             batch=batch,
@@ -518,7 +476,7 @@ def train_grpo(
         r1_count_xml
     ],
     args: GRPOTrainingArgs = GRPOTrainingArgs(),
-    loss: callable = grpo_loss,
+    loss_fn: callable = grpo_loss,
     iterate_batches: callable = iterate_grpo_batches,
     training_callback: TrainingCallback = None,
 ):
@@ -546,7 +504,7 @@ def train_grpo(
             group_size=args.group_size,
             epsilon=args.epsilon,
             ref_model=ref_model,
-            max_tokens=args.max_seq_length,
+            max_tokens=args.max_completion_length,
         )
 
         # All reduce the gradients if running in distributed mode
@@ -557,22 +515,23 @@ def train_grpo(
 
         return loss, toks, metrics
     
-    loss_value_and_grad = nn.value_and_grad(model, loss)
+    loss_value_and_grad = nn.value_and_grad(model, loss_fn)
 
     losses = 0
     n_tokens = 0
     steps = 0
     trained_tokens = 0
     accumulated_metrics = {
-        'rewards': 0,
-        'rewards_std': 0,
-        'grouped_rewards': 0,
+        'total_rewards_mean': 0,
+        'total_rewards_std': 0,
+        'grouped_rewards_mean': 0,
         'grouped_rewards_std': 0,
         'kl': 0
     }
-    for i in range(len(reward_funcs)):
-        accumulated_metrics[f'reward_func_{i}_mean'] = 0
-        accumulated_metrics[f'reward_func_{i}_std'] = 0
+    for reward_func in reward_funcs:
+        func_name = reward_func.__name__
+        accumulated_metrics[f'{func_name}_mean'] = 0
+        accumulated_metrics[f'{func_name}_std'] = 0
 
     start = time.perf_counter()
     for it, batch in zip(
@@ -592,7 +551,7 @@ def train_grpo(
             val_loss, val_ntokens, val_metrics = evaluate_grpo(
                 model=model,
                 dataset=val_dataset,
-                loss=loss,
+                loss_fn=loss_fn,
                 ref_model=ref_model,
                 reward_funcs=reward_funcs,
                 tokenizer=tokenizer,
@@ -675,8 +634,8 @@ def train_grpo(
                 for i, reward_func in enumerate(reward_funcs):
                     func_name = reward_func.__name__
                     train_metrics_str += (
-                        f", Reward func {reward_func.__name__} mean {avg_metrics[f'reward_func_{reward_func.__name__}_mean']:.3f}, "
-                        f"Reward func {reward_func.__name__} std {avg_metrics[f'reward_func_{reward_func.__name__}_std']:.3f}"
+                        f", {func_name} mean {avg_metrics[f'{func_name}_mean']:.3f}, "
+                        f"{func_name} std {avg_metrics[f'{func_name}_std']:.3f}"
                     )
 
                 print(