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run_qwen2.5-32b.sh
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set -x
gsm8k_train_path=$HOME/data/gsm8k/train.parquet
gsm8k_test_path=$HOME/data/gsm8k/test.parquet
math_train_path=$HOME/data/math/train.parquet
math_test_path=$HOME/data/math/test.parquet
train_files="['$gsm8k_train_path', '$math_train_path']"
test_files="['$gsm8k_test_path', '$math_test_path']"
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=gae \
data.train_files="$train_files" \
data.val_files="$test_files" \
data.train_batch_size=1024 \
data.max_prompt_length=1024 \
data.max_response_length=1024 \
data.filter_overlong_prompts=True \
data.truncation='error' \
actor_rollout_ref.model.path=Qwen/Qwen2.5-32B-Instruct \
actor_rollout_ref.model.enable_gradient_checkpointing=False \
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=8 \
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.use_kl_loss=False \
actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \
actor_rollout_ref.rollout.tensor_model_parallel_size=4 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \
critic.optim.lr=1e-5 \
critic.model.use_remove_padding=True \
critic.model.path=Qwen/Qwen2.5-32B-Instruct \
critic.model.enable_gradient_checkpointing=False \
critic.ppo_micro_batch_size_per_gpu=8 \
critic.model.fsdp_config.param_offload=False \
critic.model.fsdp_config.optimizer_offload=False \
algorithm.use_kl_in_reward=False \
trainer.critic_warmup=0 \
trainer.logger='["console","wandb"]' \
trainer.project_name='verl_example' \
trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \
trainer.n_gpus_per_node=8 \
trainer.nnodes=4 \
trainer.save_freq=20 \
trainer.test_freq=10 \
trainer.total_epochs=15 $@