diff --git a/src/llmtuner/tuner/ppo/trainer.py b/src/llmtuner/tuner/ppo/trainer.py
index ce5ecb98..12d05aa9 100644
--- a/src/llmtuner/tuner/ppo/trainer.py
+++ b/src/llmtuner/tuner/ppo/trainer.py
@@ -110,9 +110,17 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             # Run PPO step
             stats = self.step(queries, responses, rewards)
             self.tokenizer.padding_side = "left" # restore padding side
-            loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
+            loss_meter.update(float(stats["ppo/loss/total"]), n=len(rewards))
             reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))
 
+            if self.config.log_with is not None:
+                try:
+                    batch["query"] = self.tokenizer.batch_decode(queries, skip_special_tokens=True)
+                    batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True)
+                    self.log_stats(stats, batch, rewards)
+                except:
+                    logger.warning("Failed to save stats due to unknown errors.")
+
             self.state.global_step += 1
             self.log_callback.on_step_end(self.args, self.state, self.control)
 
diff --git a/src/llmtuner/tuner/ppo/workflow.py b/src/llmtuner/tuner/ppo/workflow.py
index ed00b437..bfc58cb6 100644
--- a/src/llmtuner/tuner/ppo/workflow.py
+++ b/src/llmtuner/tuner/ppo/workflow.py
@@ -42,6 +42,7 @@ 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,
         accelerator_kwargs={"step_scheduler_with_optimizer": False}
     )