diff --git a/trinity/trainer/verl/fsdp_checkpoint_manager.py b/trinity/trainer/verl/fsdp_checkpoint_manager.py
index 576e1e45fa..c168270366 100644
--- a/trinity/trainer/verl/fsdp_checkpoint_manager.py
+++ b/trinity/trainer/verl/fsdp_checkpoint_manager.py
@@ -360,6 +360,8 @@ def save_checkpoint(  # noqa: C901
                 if self._save_model_thread is not None:
                     self._save_model_thread.join()
 
+                state_dict = {k: v.to(torch.bfloat16) for k, v in state_dict.items()}
+
                 def _save_model():
                     runtime_context = ray.get_runtime_context()
                     node_id = runtime_context.get_node_id()
diff --git a/trinity/trainer/verl/megatron_checkpoint_manager.py b/trinity/trainer/verl/megatron_checkpoint_manager.py
index f24c8aa4ef..8674934452 100644
--- a/trinity/trainer/verl/megatron_checkpoint_manager.py
+++ b/trinity/trainer/verl/megatron_checkpoint_manager.py
@@ -257,15 +257,16 @@ def save_checkpoint(  # noqa: C901
                             from transformers import MistralForSequenceClassification
 
                             model = MistralForSequenceClassification.from_pretrained(
-                                self.config.model.path
+                                self.config.model.path, torch_dtype=torch.bfloat16
                             )  # use score head instead of lm_head
                             state_dict["score.weight"] = state_dict["score.weight"]
                         else:
                             from transformers import AutoModelForCausalLM
 
                             model = AutoModelForCausalLM.from_pretrained(
-                                self.config.model.path, torch_dtype="auto"
+                                self.config.model.path, torch_dtype=torch.bfloat16
                             )
+                    state_dict = {k: v.to(torch.bfloat16) for k, v in state_dict.items()}
                     model.save_pretrained(hf_model_ckpt_path, state_dict=state_dict)
                     log_with_rank(
                         f"Saved Huggingface config and tokenizer to {hf_model_ckpt_path}",