examples/grpo_trainer/run_gptoss_20b.sh

#!/bin/bash

cat > get_model.py << EOF
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Mxfp4Config

model_id = "openai/gpt-oss-20b"
output_dir = "$HOME/models/gpt-oss-20b-bf16"

quantization_config = Mxfp4Config(dequantize=True)
model_kwargs = dict(
    attn_implementation="eager",
    torch_dtype=torch.bfloat16,
    quantization_config=quantization_config,
    use_cache=False,
    device_map="auto",
)

model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)

# Patch config with custom attribute before saving
model.config.attn_implementation = "eager"

model.save_pretrained(output_dir)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(output_dir)
EOF

python get_model.py
# or you can use lmsys/gpt-oss-20b-bf16
# recommend to use same value for train_batch_size and ppo_mini_batch_size
# to avoid MOE training instability
# use large value for max_response_length if you want to use reasoning effort high.


model_dir=$HOME/models/gpt-oss-20b-bf16
python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files="$gsm8k_train_path" \
    data.val_files="$gsm8k_test_path" \
    data.train_batch_size=256 \
    data.max_prompt_length=512 \
    data.max_response_length=8192 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    +data.apply_chat_template_kwargs.reasoning_effort=medium \
    actor_rollout_ref.model.path=${model_dir} \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \
    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.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.actor.fsdp_config.model_dtype=bfloat16 \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=sglang \
    actor_rollout_ref.rollout.mode=sync \
    actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend=triton \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
    actor_rollout_ref.rollout.n=5 \
    actor_rollout_ref.rollout.load_format=safetensors \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger='["console","wandb"]' \
    trainer.project_name='verl_grpo_example_gsm8k_math' \
    trainer.experiment_name='oai_oss_20b_function_rm' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=50 \
    trainer.test_freq=10 \
    trainer.total_epochs=15 $@