set -x

export VLLM_ATTENTION_BACKEND=XFORMERS

python3 -m recipe.prime.main_prime \
    data.train_files=$HOME/data/gsm8k/train.parquet \
    data.val_files=$HOME/data/gsm8k/test.parquet \
    data.train_batch_size=32 \
    data.max_prompt_length=512 \
    data.max_response_length=512 \
    data.filter_accuracy=True \
    data.accuracy_lower_bound=0.2 \
    data.accuracy_upper_bound=0.8 \
    data.oversample_factor=4 \
    data.return_raw_chat=True \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B \
    actor_rollout_ref.actor.optim.lr=5e-7 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=32 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.model.enable_gradient_checkpointing=False \
    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=1 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.n=4 \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.adv_estimator=rloo \
    reward_model.model.path=Qwen/Qwen2.5-0.5B \
    reward_model.micro_batch_size_per_gpu=1 \
    reward_model.model.update=before \
    reward_model.model.beta_train=0.05 \
    reward_model.model.optim.lr=1e-6 \
    reward_model.model.optim.grad_clip=10.0 \
    reward_model.model.input_tokenizer=null \
    reward_model.mini_batch_size=32 \
    reward_model.reward_manager=naive \
    trainer.val_before_train=False \
    trainer.logger=['console'] \
    trainer.project_name='verl_example' \
    trainer.experiment_name='Qwen2.5-0.5B-PRIME' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=-1 \
    trainer.total_training_steps=1 $@
