| #!/usr/bin/env bash |
| set -e |
|
|
| cd /workspace/RAGEN |
| export PYTHONPATH="$PWD:$PWD/verl" |
| export NCCL_DEBUG=WARN |
|
|
| USE_GRPO="algorithm.adv_estimator=grpo agent_proxy.reward_normalization.method=mean_std actor_rollout_ref.actor.use_kl_loss=True" |
| USE_PPO="algorithm.adv_estimator=gae" |
|
|
| python train.py --config-name _10_deepcoder $USE_PPO\ |
| model_path="Qwen/Qwen2.5-3B-Instruct" \ |
| trainer.project_name=deepcoder_RAGEN_final \ |
| trainer.experiment_name=deepcoder_3binstructppo_200turns_test_3_filter \ |
| trainer.total_training_steps=200 \ |
| actor_rollout_ref.nccl_timeout=120 \ |
| ppo_mini_batch_size=4 \ |
| micro_batch_size_per_gpu=2 \ |
| es_manager.train.env_groups=16 es_manager.train.group_size=8 es_manager.train.env_configs.tags=["DeepCoder"] es_manager.train.env_configs.n_groups=[16] \ |
| es_manager.val.env_groups=128 es_manager.val.group_size=1 es_manager.val.env_configs.tags=["DeepCoder"] es_manager.val.env_configs.n_groups=[128] \ |
| system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ |
| trainer.save_freq=20 trainer.validation_steps=1 trainer.val_before_train=True \ |
| trainer.test_freq=10 \ |
| actor_rollout_ref.nccl_timeout=120 \ |
| actor_rollout_ref.rollout.rollout_filter_value=0.9 \ |
| actor_rollout_ref.rollout.rollout_filter_strategy=top_p \ |
| actor_rollout_ref.rollout.rollout_filter_type=largest \ |
| actor_rollout_ref.rollout.rollout_filter_include_zero=False \ |
| actor_rollout_ref.rollout.rollout_filter_top_p_prob_mode=linear \ |
| trainer.nnodes=1 \ |
| agent_proxy.max_turn=1 \ |
| actor_rollout_ref.actor.use_ref=False \ |
| actor_rollout_ref.rollout.max_model_len=6000 \ |
| actor_rollout_ref.rollout.max_num_batched_tokens=6000 \ |
| actor_rollout_ref.rollout.response_length=5000 \ |
| lora.rank=0 lora.alpha=64 lora.target_modules=all-linear \ |
| actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ |
| trainer.resume_mode=disable |
