diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py
index 5bdeba9608..c0509a77f8 100644
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -122,6 +122,14 @@ class RLHFArguments:
         default="lora",
         metadata={"help": "The checkpoint type of the reward model. The lora type only supports lora training."}
     )
+    ppo_use_separate_value_model: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Use a separate value model which does not share parameters with policy."}
+    )
+    value_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory containing the checkpoints of the value model."}
+    )
 
 
 @dataclass
diff --git a/src/llmtuner/train/ppo/trainer.py b/src/llmtuner/train/ppo/trainer.py
index acd78b0ef5..befeac7ed3 100644
--- a/src/llmtuner/train/ppo/trainer.py
+++ b/src/llmtuner/train/ppo/trainer.py
@@ -3,6 +3,7 @@
 import math
 import torch
 from tqdm import tqdm
+from types import MethodType
 from typing import TYPE_CHECKING, List, Optional, Tuple
 
 from transformers import BatchEncoding, GenerationConfig, Trainer, TrainerState, TrainerControl
@@ -296,7 +297,16 @@ def batched_forward_pass(
             attention_mask = input_kwargs["attention_mask"]
 
             with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
-                logits, _, values = model(**input_kwargs)
+                unwrapped_model = self.accelerator.unwrap_model(model)
+                if "value" in unwrapped_model.pretrained_model.peft_config:
+                    # this model has a separate value model and policy model
+                    unwrapped_model.pretrained_model.set_adapter("value")
+                    _, _, values = model(**input_kwargs)
+                    unwrapped_model.pretrained_model.set_adapter("default")
+                    logits, _, _ = model(**input_kwargs)
+                else:
+                    # this model has a shared value model and policy model
+                    logits, _, values = model(**input_kwargs)
 
             if values.size(0) != input_ids.size(0): # adapt to chatglm2
                 values = torch.transpose(values, 0, 1)
diff --git a/src/llmtuner/train/ppo/workflow.py b/src/llmtuner/train/ppo/workflow.py
index 88d5e49d3f..0307ff095b 100644
--- a/src/llmtuner/train/ppo/workflow.py
+++ b/src/llmtuner/train/ppo/workflow.py
@@ -1,7 +1,10 @@
 # Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py
 
 import math
+import os
+from peft import TaskType, LoraConfig
 from trl import PPOConfig
+import torch
 from torch.optim import AdamW
 from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorWithPadding
@@ -29,6 +32,22 @@ def run_ppo(
 ):
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
+    if finetuning_args.ppo_use_separate_value_model:
+        if finetuning_args.value_model is not None:
+            model.pretrained_model.load_adapter(finetuning_args.value_model, "value", is_trainable=True)
+            state_dict = torch.load(os.path.join(finetuning_args.value_model, "pytorch_model.bin"), map_location="cpu")
+            model.load_state_dict(state_dict, strict=False)
+        else:
+            lora_config = LoraConfig(
+                task_type=TaskType.CAUSAL_LM,
+                inference_mode=False,
+                r=finetuning_args.lora_rank,
+                lora_alpha=finetuning_args.lora_alpha,
+                lora_dropout=finetuning_args.lora_dropout,
+                target_modules=finetuning_args.lora_target,
+                modules_to_save=finetuning_args.additional_target
+            )
+            model.pretrained_model.add_adapter("value", lora_config)
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
 
     tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training