diff --git a/paddlenlp/trainer/trainer.py b/paddlenlp/trainer/trainer.py
index bacfa3bdf46f..82e7b7040ec0 100644
--- a/paddlenlp/trainer/trainer.py
+++ b/paddlenlp/trainer/trainer.py
@@ -815,13 +815,7 @@ def get_metadata_file_name(path):
 
             self._load_scheduler(resume_from_checkpoint)
 
-        from .trainer_utils import ShardingOption
-
-        should_load_stage1 = self.args.sharding_parallel_degree > 1 and ShardingOption.SHARD_OP in self.args.sharding
-        logger.debug(f"should_load_stage1 = {should_load_stage1}")
-        logger.debug(f"sharded_model_from_ema = {self.args.sharded_model_from_ema}")
-
-        if should_load_stage1 and self.args.sharded_model_from_ema:
+        if self.args.sharded_model_from_ema:
             ema_states_path = os.path.join(resume_from_checkpoint, EMA_STATE_DIC, f"{dist.get_rank()}_0.distcp")
             ema_state_dict = paddle.load(ema_states_path)
             ema_master_weights = ema_state_dict.pop("master_weights", None)
@@ -832,7 +826,6 @@ def get_metadata_file_name(path):
                 ), f"{k} not in ema_master_weights, emas_master_weight keys {ema_master_weights.keys()}"
                 paddle.assign(ema_master_weights[k], opt_master_weights[k])
 
-            ema_state_dict = reshard_util.all_gather_state_dict(ema_state_dict, lambda x: True, self.sharding_group)
             self.model.set_state_dict(ema_state_dict)
         else:
 
@@ -854,7 +847,7 @@ def bf16_filtered_sharded_state_dict(sharded_state_dict):
                 comm_method=self.args.flex_ckpt_comm_method,
             )
 
-        if self.args.bf16 and (not self.args.ignore_load_lr_and_optim) and should_load_stage1:
+        if self.args.bf16 and (not self.args.ignore_load_lr_and_optim):
             opt_state_dict = self.optimizer.state_dict()
 
             def recover_params_from_master_weight(opt_state_dict, group):
diff --git a/paddlenlp/trainer/utils/zero_cost_checkpoint.py b/paddlenlp/trainer/utils/zero_cost_checkpoint.py
index 0b4b4a57990a..bb2c316a7164 100644
--- a/paddlenlp/trainer/utils/zero_cost_checkpoint.py
+++ b/paddlenlp/trainer/utils/zero_cost_checkpoint.py
@@ -1505,37 +1505,13 @@ def __init__(self, args, zcc_manager, timer, unused_arg):
             self.sharding_group = self.hcg.get_sharding_parallel_group()
 
     def _manipulate_state_dict_and_config(self, model_to_save, optimizer):
-        # return model_to_save.sharded_state_dict()
-
         group_getter = GroupGetter(model_to_save)
         gids = group_getter.get_group_ids()
-        from paddlenlp.trainer.utils.sharding_io import (
-            exclude_parameters_in_state_dict,
-            filter_sharded_params,
-        )
-
-        # filter_sharded_params = sharded_state_dict_compatibility(filter_sharded_params, return_sharded_state_dict=True)
-        # exclude_parameters_in_state_dict = sharded_state_dict_compatibility(
-        #     exclude_parameters_in_state_dict, return_sharded_state_dict=True
-        # )
+        from paddlenlp.trainer.utils.sharding_io import exclude_parameters_in_state_dict
 
         state_dict = model_to_save.state_dict()
-        # tmp wa should_save_sharding_stage1_model
-        if self.args.should_save_sharding_stage1_model or self.args.save_checkpoint_format == "flex_checkpoint":
-            state_dict = split_model_state(state_dict, group_getter)
-            for gid in gids:
-                state_dict[gid] = filter_sharded_params(
-                    state_dict.get(gid, {}),
-                    optimizer,
-                    self.sharding_group,
-                    self.args.save_sharding_stage1_model_include_freeze_params,
-                )
-            state_dict = merge_model_state(state_dict)
 
-        # tmp wa should_save_sharding_stage1_model
-        if self.args.bf16 and (
-            self.args.should_save_sharding_stage1_model or self.args.save_checkpoint_format == "flex_checkpoint"
-        ):
+        if self.args.bf16:
             param_names_in_master_weights = []
             optimzier_state_dict = optimizer.state_dict()
             optimzier_state_dict = split_opt_state(optimzier_state_dict, group_getter)