diff --git a/README.md b/README.md
index 535deefb..ceb84cb1 100644
--- a/README.md
+++ b/README.md
@@ -313,6 +313,8 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --per_device_train_batch_size 2 \
     --gradient_accumulation_steps 4 \
     --lr_scheduler_type cosine \
+    --top_k 0 \
+    --top_p 0.9 \
     --logging_steps 10 \
     --save_steps 1000 \
     --learning_rate 1e-5 \
diff --git a/README_zh.md b/README_zh.md
index 10418c3d..740dab0a 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -313,6 +313,8 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --per_device_train_batch_size 2 \
     --gradient_accumulation_steps 4 \
     --lr_scheduler_type cosine \
+    --top_k 0 \
+    --top_p 0.9 \
     --logging_steps 10 \
     --save_steps 1000 \
     --learning_rate 1e-5 \
diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py
index cfdc8b24..d39812c7 100644
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -74,6 +74,10 @@ class RLHFArguments:
         default=None,
         metadata={"help": "Log with either 'wandb' or 'tensorboard' in PPO training."}
     )
+    ppo_epochs: Optional[int] = field(
+        default=4,
+        metadata={"help": "Number of optimisation epochs per batch of samples"},
+    )
     ppo_score_norm: Optional[bool] = field(
         default=False,
         metadata={"help": "Use score normalization in PPO training."}
diff --git a/src/llmtuner/train/ppo/workflow.py b/src/llmtuner/train/ppo/workflow.py
index 2a779edd..3e7d8053 100644
--- a/src/llmtuner/train/ppo/workflow.py
+++ b/src/llmtuner/train/ppo/workflow.py
@@ -45,7 +45,7 @@ def run_ppo(
         mini_batch_size=training_args.per_device_train_batch_size,
         batch_size=training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps,
         gradient_accumulation_steps=training_args.gradient_accumulation_steps,
-        ppo_epochs=1,
+        ppo_epochs=finetuning_args.ppo_epochs,
         max_grad_norm=training_args.max_grad_norm,
         seed=training_args.seed,
         optimize_device_cache=True,