diff --git a/src/llmtuner/tuner/ppo/trainer.py b/src/llmtuner/tuner/ppo/trainer.py
index 9358adf0..cd34c531 100644
--- a/src/llmtuner/tuner/ppo/trainer.py
+++ b/src/llmtuner/tuner/ppo/trainer.py
@@ -12,7 +12,7 @@ from trl.core import LengthSampler, PPODecorators, logprobs_from_logits
 from llmtuner.extras.logging import get_logger
 from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
 from llmtuner.tuner.core.trainer import PeftTrainer
-from llmtuner.tuner.ppo.utils import replace_model
+from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments
@@ -152,8 +152,10 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         if length_sampler is not None:
             generation_kwargs["max_new_tokens"] = length_sampler()
 
+        self.model, layer_norm_params = cast_layernorm_dtype(self.model, self.compute_dtype)
         unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
         response: torch.Tensor = unwrapped_model.generate(**batch, **generation_kwargs)
+        self.model, _ = cast_layernorm_dtype(self.model, self.compute_dtype, layer_norm_params)
 
         # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
         # Inspired by: https://github.com/huggingface/transformers/blob/v4.28.1/src/transformers/trainer_seq2seq.py#L273
diff --git a/src/llmtuner/tuner/ppo/utils.py b/src/llmtuner/tuner/ppo/utils.py
index 3215ee8a..1b48c8c0 100644
--- a/src/llmtuner/tuner/ppo/utils.py
+++ b/src/llmtuner/tuner/ppo/utils.py
@@ -1,4 +1,7 @@
-from typing import TYPE_CHECKING, Literal
+import torch
+from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple
+
+from llmtuner.extras.constants import LAYERNORM_NAMES
 
 if TYPE_CHECKING:
     from trl import AutoModelForCausalLMWithValueHead
@@ -15,3 +18,23 @@ def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["d
         "summary.weight": getattr(model, "{}_head_weight".format(target)),
         "summary.bias": getattr(model, "{}_head_bias".format(target))
     })
+
+
+def cast_layernorm_dtype(
+    model: "AutoModelForCausalLMWithValueHead",
+    compute_dtype: torch.dtype,
+    layer_norm_params: Optional[Dict[str, torch.Tensor]] = None,
+    layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
+) -> Tuple["AutoModelForCausalLMWithValueHead", Dict[str, torch.Tensor]]:
+
+    layer_norm_state_dict = {}
+
+    for name, param in model.named_parameters():
+        if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
+            if layer_norm_params is None:
+                layer_norm_state_dict[name] = param.data.detach().clone() # store float32 weights for stability
+                param.data = param.data.to(compute_dtype)
+            else:
+                param.data = layer_norm_params[name] # restore float32 weights
+
+    return model, layer_norm_state_dict