We develop our code based on oat, which can be installed:
```diff
pip install vllm==0.7.2 && pip install oat-llm==0.0.9
```

Then we could run experiment using command below:
```diff
python main.py \
    --critic_type drgrpo \
    --gpus 8 \
    --enable_prefix_caching \
    --collocate \
    --vllm_sleep \
    --vllm_gpu_ratio 0.35 \
    --gradient-checkpointing \
    --flash-attn \
    --bf16 \
    --rnd-seed \
    --learning_rate 0.000001 \
    --lr_scheduler constant \
    --num_ppo_epochs 1 \
    --beta 0 \
    --oracle_type reward \
    --oracle math \
    --pretrain Qwen/Qwen2.5-Math-1.5B \
    --prompt_template r1 \
    --zero-stage 2 \
    --ref_offload \
    --prompt_data ./datasets/train/math_12k \
    --train_split train \
    --input_key problem \
    --output_key answer \
    --max-train 9999999 \
    --num_prompt_epoch 20 \
    --prompt_max_length 1024 \
    --num_samples 8 \
    --temperature 1 \
    --top_p 1 \
    --generate_max_length 3000 \
    --save_steps -1 \
    --train_batch_size 128 \
    --train_batch_size_per_device 1 \
    --mini_train_batch_size_per_device 1 \
    --rollout_batch_size 128 \
    --rollout_batch_size_per_device 16 \
    --pi_buffer_maxlen_per_device 128 \
    --eval_batch_size 200 \
    --eval_steps 16 \
    --eval_temperature 0 \
    --eval_generate_max_length 3000 \
    --eval_data ./datasets/evaluation_suite \
    --eval_input_key input
```
