diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py
index 09ed25d3..a153e440 100644
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -57,7 +57,15 @@ class FinetuningArguments:
     )
     ppo_score_norm: Optional[bool] = field(
         default=False,
-        metadata={"help": "Use score normalization in PPO Training."}
+        metadata={"help": "Use score normalization in PPO training."}
+    )
+    ppo_logger: Optional[str] = field(
+        default=None,
+        metadata={"help": "Log with either 'wandb' or 'tensorboard' in PPO training."}
+    )
+    ppo_target: Optional[float] = field(
+        default=6.0,
+        metadata={"help": "Target KL value for adaptive KL control in PPO training."}
     )
     dpo_beta: Optional[float] = field(
         default=0.1,
diff --git a/src/llmtuner/tuner/core/parser.py b/src/llmtuner/tuner/core/parser.py
index a8ed914a..118138c2 100644
--- a/src/llmtuner/tuner/core/parser.py
+++ b/src/llmtuner/tuner/core/parser.py
@@ -201,7 +201,9 @@ def get_train_args(
             )
 
     # postprocess model_args
-    model_args.compute_dtype = torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None)
+    model_args.compute_dtype = (
+        torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else torch.float32)
+    )
     model_args.model_max_length = data_args.cutoff_len
 
     # Log on each process the small summary:
diff --git a/src/llmtuner/tuner/ppo/trainer.py b/src/llmtuner/tuner/ppo/trainer.py
index 85e48279..44df9672 100644
--- a/src/llmtuner/tuner/ppo/trainer.py
+++ b/src/llmtuner/tuner/ppo/trainer.py
@@ -206,7 +206,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         replace_model(unwrapped_model, target="reward")
         batch = self.prepare_model_inputs(queries, responses)
 
-        with torch.cuda.amp.autocast(dtype=self.compute_dtype): # support bf16
+        with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
             _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
 
         if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
@@ -251,7 +251,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             input_ids = input_kwargs["input_ids"]
             attention_mask = input_kwargs["attention_mask"]
 
-            with torch.cuda.amp.autocast(dtype=self.compute_dtype): # support bf16
+            with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
                 logits, _, values = model(**input_kwargs)
 
             if values.size(0) != input_ids.size(0): # adapt to chatglm2
diff --git a/src/llmtuner/tuner/ppo/workflow.py b/src/llmtuner/tuner/ppo/workflow.py
index 7fd2f29b..1dd3205e 100644
--- a/src/llmtuner/tuner/ppo/workflow.py
+++ b/src/llmtuner/tuner/ppo/workflow.py
@@ -42,15 +42,14 @@ def run_ppo(
         ppo_epochs=1,
         max_grad_norm=training_args.max_grad_norm,
         seed=training_args.seed,
-        log_with=training_args.report_to,
         optimize_cuda_cache=True,
+        target=finetuning_args.ppo_target,
+        log_with=finetuning_args.ppo_logger,
+        use_score_scaling=finetuning_args.ppo_score_norm,
+        use_score_norm=finetuning_args.ppo_score_norm,
         accelerator_kwargs={"step_scheduler_with_optimizer": False}
     )
 
-    if finetuning_args.ppo_score_norm:
-        ppo_config.use_score_scaling = True
-        ppo_config.use_score_norm = True
-
     optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
     total_train_batch_size = (
         training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size