diff --git a/.github/workflows/run_chatgpt_examples.yml b/.github/workflows/run_chatgpt_examples.yml
index 1d8240ad4631..9d9d3a007851 100644
--- a/.github/workflows/run_chatgpt_examples.yml
+++ b/.github/workflows/run_chatgpt_examples.yml
@@ -13,6 +13,10 @@ on:
 jobs:
   tests:
     name: Run ChatGPT examples
+    if: |
+      github.event.pull_request.draft == false &&
+      github.base_ref == 'main' &&
+      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
     runs-on: [self-hosted, gpu]
     container:
       image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
diff --git a/.github/workflows/run_chatgpt_unit_tests.yml b/.github/workflows/run_chatgpt_unit_tests.yml
index 4e539bfe06fd..407f630e2469 100644
--- a/.github/workflows/run_chatgpt_unit_tests.yml
+++ b/.github/workflows/run_chatgpt_unit_tests.yml
@@ -4,16 +4,20 @@ on:
   pull_request:
     types: [synchronize, opened, reopened]
     paths:
-      - 'applications/ChatGPT/chatgpt/**'
-      - 'applications/ChatGPT/requirements.txt'
-      - 'applications/ChatGPT/setup.py'
-      - 'applications/ChatGPT/requirements-test.txt'
-      - 'applications/ChatGPT/tests/**'
-      - 'applications/ChatGPT/pytest.ini'
+      - 'applications/Chat/coati/**'
+      - 'applications/Chat/requirements.txt'
+      - 'applications/Chat/setup.py'
+      - 'applications/Chat/requirements-test.txt'
+      - 'applications/Chat/tests/**'
+      - 'applications/Chat/pytest.ini'
 
 jobs:
   tests:
     name: Run ChatGPT unit tests
+    if: |
+      github.event.pull_request.draft == false &&
+      github.base_ref == 'main' &&
+      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
     runs-on: [self-hosted, gpu]
     container:
       image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py
index 2db604fc9b74..cf752549501f 100644
--- a/applications/Chat/coati/trainer/ppo.py
+++ b/applications/Chat/coati/trainer/ppo.py
@@ -210,9 +210,6 @@ def training_step(self, experience: Experience) -> Dict[str, float]:
     def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
         self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer)
 
-    def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
-        self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer)
-
 
 def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None:
     origin_model = strategy._unwrap_actor(actor)
diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py
index 5ded6d8432ed..2086ff003e34 100644
--- a/applications/Chat/examples/train_prompts.py
+++ b/applications/Chat/examples/train_prompts.py
@@ -8,7 +8,7 @@
 from coati.models.gpt import GPTRM, GPTActor, GPTCritic
 from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM
 from coati.models.opt import OPTRM, OPTActor, OPTCritic
-from coati.models.roberta import RoBERTaRM, RoBERTaActor, RoBERTaCritic
+from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM
 from coati.trainer import PPOTrainer
 from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
 from coati.utils import prepare_llama_tokenizer_and_embedding
@@ -143,6 +143,8 @@ def main(args):
     prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384)
     if dist.is_initialized() and dist.get_world_size() > 1:
         prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True)
+    else:
+        prompt_sampler = None
     prompt_dataloader = DataLoader(prompt_dataset,
                                    shuffle=(prompt_sampler is None),
                                    sampler=prompt_sampler,
@@ -151,6 +153,8 @@ def main(args):
     pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384)
     if dist.is_initialized() and dist.get_world_size() > 1:
         pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True)
+    else:
+        pretrain_sampler = None
     pretrain_dataloader = DataLoader(pretrain_dataset,
                                      shuffle=(pretrain_sampler is None),
                                      sampler=pretrain_sampler,