

set -x

train_data_path="<path_to_train_data>"
test_data_path="<path_to_test_data>"

reward_function_path="<path_to_reward_function>"

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files="$train_data_path" \
    data.val_files="$test_data_path" \
    data.train_batch_size=512 \
    data.max_prompt_length=10000 \
    data.max_response_length=10000 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.shuffle=False \
    actor_rollout_ref.model.path="<path_to_model>" \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.ppo_mini_batch_size=128 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.actor.ppo_epochs=2 \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.clip_ratio=0.2 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=False \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
    actor_rollout_ref.rollout.n=6 \
    actor_rollout_ref.rollout.temperature=0.8 \
    actor_rollout_ref.rollout.top_p=0.9 \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.use_kl_in_reward=False \
    custom_reward_function.path="$reward_function_path" \
    custom_reward_function.name="<reward_function_name>" \
    reward_model.enable=False \
    trainer.critic_warmup=0 \
    trainer.logger='["console","wandb"]' \
    trainer.project_name='<project_name>' \
    trainer.experiment_name='<experiment_name>' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=10 \
    trainer.test_freq=5 \
    trainer.total_epochs=20 \
    $@