set -e # Section 1: Base Experiments USE_GRPO="algorithm.adv_estimator=grpo" # 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" # by default. USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # python train.py --config-name _8_2048 system.CUDA_VISIBLE_DEVICES="'0,1,2,3'" trainer.project_name=ragen_latest_qwen_25_15b_it trainer.n_gpus_per_node=4 model_path=Qwen/Qwen2.5-0.5B-Instruct trainer.experiment_name=game_2048 $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1'" trainer.project_name=ragen_latest_qwen_05B_it trainer.n_gpus_per_node=2 model_path=Qwen/Qwen2.5-0.5B-Instruct trainer.experiment_name=rubikscube-1 $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'2,3'" trainer.project_name=ragen_latest_qwen_25_15b_it trainer.n_gpus_per_node=2 model_path=Qwen/Qwen2.5-1.5B-Instruct trainer.experiment_name=rubikscube-2 $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'4,5'" trainer.project_name=ragen_latest_qwen_25_3b_it trainer.n_gpus_per_node=2 model_path=Qwen/Qwen2.5-3B-Instruct trainer.experiment_name=rubikscube-2 $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'6,7'" trainer.project_name=ragen_latest_qwen_05B_it trainer.n_gpus_per_node=2 model_path=Qwen/Qwen2.5-0.5B-Instruct trainer.experiment_name=rubikscube-3 $USE_PPO $USE_BASE # Section 3.1&3.2 - General Observations # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="'6,7'" trainer.project_name=ragen_latest_qwen_25_15b_it trainer.n_gpus_per_node=2 model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_1.5B_it_multitask trainer.experiment_name=bandit-ppo-multitask $USE_PPO $USE_BASE & # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="'7'" trainer.n_gpus_per_node=1 trainer.experiment_name=bandit-ppo-frommlp $USE_PPO $USE_BASE # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="'0,1'" trainer.project_name=ragen_latest_qwen_25_15b_it trainer.n_gpus_per_node=2 model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_1.5B_it_multitask trainer.experiment_name=sokoban-ppo-box1-multitask $USE_PPO $USE_BASE # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=sokoban-grpo $USE_GRPO $USE_BASE & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="'2,3'" trainer.project_name=ragen_latest_qwen_25_15b_it trainer.n_gpus_per_node=2 model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_1.5B_it_multitask trainer.experiment_name=frozen_lake-ppo-slippery-multitask $USE_PPO $USE_BASE # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="'4,5,6,7'" trainer.project_name=ragen_latest_qwen_25_3b_it trainer.n_gpus_per_node=4 model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_multitask trainer.experiment_name=frozen_lake-ppo-slippery-multitask $USE_PPO $USE_BASE # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="'4,5,6,7'" trainer.n_gpus_per_node=4 model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_1.5B_it_multitask trainer.experiment_name=frozen_lake-ppo-slippery-multitask $USE_PPO $USE_BASE # # Section 4.1 - Filtering and critic # # 0.25 # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="4" trainer.experiment_name=sokoban-ppo-rolloutfilter0.25 actor_rollout_ref.rollout.rollout_filter_ratio=0.25 $USE_PPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=sokoban-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.25 $USE_GRPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=frozen_lake-ppo actor_rollout_ref.rollout.rollout_filter_ratio=0.25 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=frozen_lake-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.25 $USE_GRPO & # wait # # 0.5 # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-ppo-rolloutfilter0.5 actor_rollout_ref.rollout.rollout_filter_ratio=0.5 $USE_PPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.5 $USE_GRPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=frozen_lake-ppo actor_rollout_ref.rollout.rollout_filter_ratio=0.5 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=frozen_lake-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.5 $USE_GRPO & # # 0.75 # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="4" trainer.experiment_name=sokoban-ppo-rolloutfilter0.75 actor_rollout_ref.rollout.rollout_filter_ratio=0.75 $USE_PPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=sokoban-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.75 $USE_GRPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=frozen_lake-ppo actor_rollout_ref.rollout.rollout_filter_ratio=0.75 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=frozen_lake-grpo actor_rollout_ref.rollout.rollout_filter_ratio=0.75 $USE_GRPO & # wait # # Section 4.2 - Ablation on Critic/ClipHigh/KL. Start from Basic and add more components. The best setting for StarPO in agent is rollout_filter+Critic+Cliphigh+NoKL # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-base-grpo algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_GRPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-base-ppo algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=sokoban-base-ppo-cliphigh algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.28 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=sokoban-base-ppo-nokl algorithm.kl_ctrl.kl_coef=0.000 actor_rollout_ref.actor.kl_loss_coef=0.000 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="4" trainer.experiment_name=frozenlake-base-grpo algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_GRPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=frozenlake-base-ppo algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=frozenlake-base-ppo-cliphigh algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.28 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=frozenlake-base-ppo-nokl algorithm.kl_ctrl.kl_coef=0.000 actor_rollout_ref.actor.kl_loss_coef=0.000 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1 $USE_PPO & # wait # # Section 5.1 - Reasoning Helps Generalization # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=bandit-generalization \ # custom_envs.Bandit.env_config.lo_arm_name="Engineer" \ # custom_envs.Bandit.env_config.hi_arm_name="Teacher" \ # custom_envs.BanditTest.env_config.lo_arm_name="Trader" \ # custom_envs.BanditTest.env_config.hi_arm_name="Librarian" # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=bandit-generalization-nothink \ # custom_envs.Bandit.env_config.lo_arm_name="Engineer" \ # custom_envs.Bandit.env_config.hi_arm_name="Teacher" \ # custom_envs.BanditTest.env_config.lo_arm_name="Trader" \ # custom_envs.BanditTest.env_config.hi_arm_name="Librarian" \ # agent_proxy.enable_think=False # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=bandit-generalization-rev \ # custom_envs.Bandit.env_config.lo_arm_name="Teacher" \ # custom_envs.Bandit.env_config.hi_arm_name="Engineer" \ # custom_envs.BanditTest.env_config.lo_arm_name="Librarian" \ # custom_envs.BanditTest.env_config.hi_arm_name="Trader" # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=bandit-generalization-rev-nothink \ # custom_envs.Bandit.env_config.lo_arm_name="Teacher" \ # custom_envs.Bandit.env_config.hi_arm_name="Engineer" \ # custom_envs.BanditTest.env_config.lo_arm_name="Librarian" \ # custom_envs.BanditTest.env_config.hi_arm_name="Trader" \ # agent_proxy.enable_think=False # SOKOBAN_GENERALIZATION_CONFIG="es_manager.val.env_groups=512 es_manager.val.group_size=1 es_manager.val.env_configs.tags=[SimpleSokoban,LargerSokoban,SokobanDifferentGridVocab,FrozenLake] es_manager.val.env_configs.n_groups=[128,128,128,128]" # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-generalization $SOKOBAN_GENERALIZATION_CONFIG trainer.total_training_steps=500 trainer.save_freq=50 trainer.default_local_dir=/mnt/local/ragen_checkpoints/sokoban-generalization micro_batch_size_per_gpu=8 model_path=Qwen/Qwen2.5-1.5B-Instruct& # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=sokoban-generalization-nothink $SOKOBAN_GENERALIZATION_CONFIG agent_proxy.enable_think=False & # # SOKOBAN_GENERALIZATION_CONFIG="es_manager.val.env_groups=128 es_manager.val.group_size=1 es_manager.val.env_configs.tags=[SokobanDifferentGridVocab] es_manager.val.env_configs.n_groups=[128]" # # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-generalization $SOKOBAN_GENERALIZATION_CONFIG & # # COMPOSITIONALITY_CONFIG="es_manager.train.env_groups=16 es_manager.train.env_configs.tags=[Bandit,SimpleSokoban] es_manager.train.env_configs.n_groups=[8,8] es_manager.val.env_groups=512 es_manager.val.group_size=1 es_manager.val.env_configs.tags=[Bandit,SimpleSokoban,LargerSokoban,FrozenLake] es_manager.val.env_configs.n_groups=[128,128,128,128] actor_rollout_ref.rollout.rollout_filter_ratio=1" # NOTE that we don't filter out low-var rollout in this setting # # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=compositional-generalization $COMPOSITIONALITY_CONFIG & # # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=compositional-generalization-nothink $COMPOSITIONALITY_CONFIG agent_proxy.enable_think=False & # wait # # Section 5.2 - what leads to better reasoning? # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-generalization-qwen2.5-0.5b-instruct $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-0.5B-Instruct & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-generalization-qwen2.5-1.5b-instruct $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-1.5B-Instruct & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"2,3\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-generalization-qwen2.5-3b-instruct $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-3B-Instruct trainer.n_gpus_per_node=2 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"4,5,6,7\" trainer.n_gpus_per_node=4 trainer.experiment_name=sokoban-generalization-qwen2.5-7b-instruct $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-7B-Instruct trainer.n_gpus_per_node=4 & # wait # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-generalization-qwen2.5-0.5b $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-0.5B & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-generalization-qwen2.5-1.5b $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-1.5B & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"2,3\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-generalization-qwen2.5-3b $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-3B & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"4,5,6,7\" trainer.n_gpus_per_node=4 trainer.experiment_name=sokoban-generalization-qwen2.5-7b $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-7B & # wait # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1,2,3\" trainer.n_gpus_per_node=4 trainer.n_gpus_per_node=4 trainer.experiment_name=sokoban-generalization-qwen2.5-7b-r1 $SOKOBAN_GENERALIZATION_CONFIG model_path=deepseek-ai/DeepSeek-R1-Distill-Qwen-7B & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"4,5\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-generalization-qwen2.5-1.5b-r1 $SOKOBAN_GENERALIZATION_CONFIG model_path=deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B & # wait # # Section 6.1 varying action count # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-action-count-1 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=5 custom_envs.LargerSokoban.max_actions_per_traj=5 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=5 custom_envs.FrozenLake.max_actions_per_traj=5 agent_proxy.max_actions_per_turn=1 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-action-count-2 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=10 custom_envs.LargerSokoban.max_actions_per_traj=10 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=10 custom_envs.FrozenLake.max_actions_per_traj=10 agent_proxy.max_actions_per_turn=2 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=sokoban-action-count-3 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=15 custom_envs.LargerSokoban.max_actions_per_traj=15 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=15 custom_envs.FrozenLake.max_actions_per_traj=15 agent_proxy.max_actions_per_turn=3 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=sokoban-action-count-4 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=20 custom_envs.LargerSokoban.max_actions_per_traj=20 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=20 custom_envs.FrozenLake.max_actions_per_traj=20 agent_proxy.max_actions_per_turn=4 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="4" trainer.experiment_name=sokoban-action-count-5 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=25 custom_envs.LargerSokoban.max_actions_per_traj=25 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=25 custom_envs.FrozenLake.max_actions_per_traj=25 agent_proxy.max_actions_per_turn=5 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=sokoban-action-count-6 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=30 custom_envs.LargerSokoban.max_actions_per_traj=30 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=30 custom_envs.FrozenLake.max_actions_per_traj=30 agent_proxy.max_actions_per_turn=6 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=sokoban-action-count-7 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=35 custom_envs.LargerSokoban.max_actions_per_traj=35 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=35 custom_envs.FrozenLake.max_actions_per_traj=35 agent_proxy.max_actions_per_turn=7 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=sokoban-action-count-8 $SOKOBAN_GENERALIZATION_CONFIG custom_envs.SimpleSokoban.max_actions_per_traj=40 custom_envs.LargerSokoban.max_actions_per_traj=40 custom_envs.SokobanDifferentGridVocab.max_actions_per_traj=40 custom_envs.FrozenLake.max_actions_per_traj=40 agent_proxy.max_actions_per_turn=8 & # # section 6.2 Varying prompt diversity # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-prompt-diversity-4 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=4 es_manager.train.group_size=32 es_manager.train.env_configs.n_groups=[4] & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=sokoban-prompt-diversity-8 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=8 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[8] & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=sokoban-prompt-diversity-16 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=16 es_manager.train.group_size=8 es_manager.train.env_configs.n_groups=[16] & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="3" trainer.experiment_name=sokoban-prompt-diversity-32 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=32 es_manager.train.group_size=4 es_manager.train.env_configs.n_groups=[32] & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="4" trainer.experiment_name=sokoban-prompt-diversity-64 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=64 es_manager.train.group_size=2 es_manager.train.env_configs.n_groups=[64] & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=sokoban-prompt-diversity-128 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=128 es_manager.train.group_size=1 es_manager.train.env_configs.n_groups=[128] & # wait # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="6" trainer.experiment_name=sokoban-online-2 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=16 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[16] trainer.total_training_steps=100 trainer.test_freq=5 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="7" trainer.experiment_name=sokoban-online-5 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=40 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[40] trainer.total_training_steps=40 trainer.test_freq=2 & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="0" trainer.experiment_name=sokoban-online-10 $SOKOBAN_GENERALIZATION_CONFIG es_manager.train.env_groups=80 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[80] trainer.total_training_steps=80 trainer.test_freq=1 & # # Extension: Training 7B reasoning model # SOKOBAN_GENERALIZATION_CONFIG="es_manager.val.env_groups=512 es_manager.val.group_size=1 es_manager.val.env_configs.tags=[SimpleSokoban,LargerSokoban,SokobanDifferentGridVocab,FrozenLake] es_manager.val.env_configs.n_groups=[128,128,128,128]" # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1,2,3,4,5,6,7\" trainer.n_gpus_per_node=8 trainer.experiment_name=sokoban-generalization-qwen2.5-3b-instruct-largescale $SOKOBAN_GENERALIZATION_CONFIG model_path=Qwen/Qwen2.5-3B-Instruct es_manager.train.env_groups=8 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[8] micro_batch_size_per_gpu=8 ppo_mini_batch_size=64 actor_rollout_ref.rollout.response_length=1024 actor_rollout_ref.rollout.max_model_len=6400 trainer.test_freq=5 actor_rollout_ref.rollout.max_num_batched_tokens=24000 micro_batch_size_per_gpu=2 actor_rollout_ref.rollout.rollout_filter_ratio=1 & # python -m ragen.llm_agent.agent_proxy model_path=Qwen/Qwen2.5-3B-Instruct system.CUDA_VISIBLE_DEVICES=\"0,1,2,3,4,5,6,7\" trainer.n_gpus_per_node=8 actor_rollout_ref.rollout.tensor_model_parallel_size=4 actor_rollout_ref.rollout.response_length=2048 actor_rollout_ref.rollout.max_model_len=12800 # # trainer.save_freq=50 trainer.default_local_dir=/mnt/local/cache/exp_name # # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0\" trainer.n_gpus_per_node=1 trainer.experiment_name=sokoban-final enable_response_mask=True trainer.total_training_steps=500 trainer.save_freq=50 trainer.default_local_dir=/mnt/local/ragen_checkpoints/sokoban-generalization & # python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES=\"1\" trainer.n_gpus_per_node=1 trainer.experiment_name=bandit-final enable_response_mask=True trainer.total_training_steps=500 trainer.save_freq=50 trainer.default_local_dir=/mnt/local/ragen_checkpoints/bandit-generalization & # python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES=\"1\" trainer.n_gpus_per_node=1 trainer.experiment_name=frozenlake-final enable_response_mask=True trainer.total_training_steps=500 trainer.save_freq=50 trainer.default_local_dir=/mnt/local/ragen_checkpoints/frozenlake-generalization & # # USE_PPO="algorithm.adv_estimator=gae" # by default. # # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0\" trainer.n_gpus_per_node=1 trainer.experiment_name=sokoban-ppo $USE_PPO $USE_BASE ppo_mini_batch_size=64 enable_response_mask=True & # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-s-grpo algorithm.adv_estimator=grpo agent_proxy.reward_normalization.method=mean_std & # # enable_response_mask: False # # grpo_advantage_length_weight: True # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 trainer.experiment_name=sokoban-s-grpo-1-5b algorithm.adv_estimator=grpo agent_proxy.reward_normalization.method=mean_std agent_proxy.max_actions_per_turn=5 custom_envs.SimpleSokoban.max_actions_per_traj=25 enable_response_mask=True grpo_advantage_length_weight=False model_path=Qwen/Qwen2.5-1.5B-Instruct & # # extension: 7B with lora. Currently NOT recommended to use lora: within current version of vllm, this could result in rollouts slower than non-lora by 100% # python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1,2,3\" trainer.n_gpus_per_node=4 model_path=Qwen/Qwen2.5-7B-Instruct trainer.experiment_name=sokoban_7b_instruct_lora_newversion lora.rank=16 # # extension: bi-level gae # python train.py trainer.experiment_name=sokoban-bi-level-gae-final \ # system.CUDA_VISIBLE_DEVICES=\"0,1,2,3\" trainer.n_gpus_per_node=4 actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ # model_path=Qwen/Qwen2.5-0.5B-Instruct \ # algorithm.bi_level_gae=True algorithm.high_level_gamma=0.95 \ # agent_proxy.use_turn_scores=True \ # actor_rollout_ref.rollout.tp_size_check=False # # extension: webshop # USE_PPO="algorithm.adv_estimator=gae" # by default. # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _6_webshop system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=webshop-3b-ppo-s $USE_PPO \ # trainer.nnodes=1 & # USE_GRPO="algorithm.adv_estimator=grpo" # by default. # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _6_webshop system.CUDA_VISIBLE_DEVICES=\"2,3\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=webshop-3b-grpo-s $USE_GRPO \ # trainer.nnodes=1 & # # StarPO ppo # USE_PPO="algorithm.adv_estimator=gae" # by default. # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _6_webshop system.CUDA_VISIBLE_DEVICES=\"4,5\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=webshop-3b-ppo $USE_PPO $USE_BASE \ # es_manager.train.env_groups=2 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[2] \ # trainer.nnodes=1 & # # StarPO grpo # USE_GRPO="algorithm.adv_estimator=grpo" # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _6_webshop system.CUDA_VISIBLE_DEVICES=\"6,7\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=webshop-3b-grpo $USE_GRPO $USE_BASE \ # es_manager.train.env_groups=2 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[2] \ # trainer.nnodes=1 & # # normal:sokoban # # extension: sokoban # USE_PPO="algorithm.adv_estimator=gae" # by default. # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=sokoban-3b-ppo-s $USE_PPO \ # trainer.nnodes=1 & # USE_GRPO="algorithm.adv_estimator=grpo" # by default. # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"2,3\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=sokoban-3b-grpo-s $USE_GRPO \ # trainer.nnodes=1 & # # StarPO ppo # USE_PPO="algorithm.adv_estimator=gae" # by default. # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"4,5\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=sokoban-3b-ppo $USE_PPO $USE_BASE \ # es_manager.train.env_groups=2 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[2] \ # trainer.nnodes=1 & # # StarPO grpo # USE_GRPO="algorithm.adv_estimator=grpo" # USE_BASE="algorithm.kl_ctrl.kl_coef=0.001 actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.clip_ratio_high=0.2 actor_rollout_ref.rollout.rollout_filter_ratio=1" # MKL_SERVICE_FORCE_INTEL=1 python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES=\"6,7\" trainer.n_gpus_per_node=2 \ # trainer.experiment_name=sokoban-3b-grpo $USE_GRPO $USE_BASE \ # es_manager.train.env_groups=2 es_manager.train.group_size=16 es_manager.train.env_configs.n_groups=[2] \ # trainer.nnodes=1 & # python train.py \ # trainer.experiment_name=3b-full-ppo-test system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2 # python train.py --config-name _11_sudoku \ # system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it\ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sudoku4x4_nohint_think_sa \ # trainer.experiment_name=sudoku-4x4-nohint-withthink_sa $USE_PPO $USE_BASE python train.py --config-name _2_sokoban \ system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ trainer.project_name=ragen_latest_qwen_25_3b_it\ trainer.n_gpus_per_node=8 \ custom_envs.CoordSokoban.env_config.num_boxes=1 \ model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_withthink_fulltracj_sa \ trainer.experiment_name=sokoban-box1-withthink_fulltrajc_sa $USE_PPO $USE_BASE \ trainer.save_freq=200 \ trainer.default_local_dir=/mnt/general/wanghy/RAGEN_v2/saves/qwen2.5_3B_it_sokoban1_withthink_fulltraj_sa_rl python train.py --config-name _2_sokoban \ system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ trainer.project_name=ragen_latest_qwen_25_3b_it\ trainer.n_gpus_per_node=8 \ custom_envs.CoordSokoban.env_config.num_boxes=2 \ model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box2_withthink_fulltracj_sa \ trainer.experiment_name=sokoban-box2-withthink_fulltrajc_sa $USE_PPO $USE_BASE \ trainer.save_freq=200 \ trainer.default_local_dir=/mnt/general/wanghy/RAGEN_v2/saves/qwen2.5_3B_it_sokoban2_withthink_fulltraj_sa_rl python train.py --config-name _11_sudoku \ system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ trainer.project_name=ragen_latest_qwen_25_3b_it\ trainer.n_gpus_per_node=8 \ model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sudoku4x4_nohint_think_sa_fulltrajc \ trainer.experiment_name=sudoku_3b_think_sa_fulltrajc $USE_PPO $USE_BASE \ trainer.save_freq=200 \ trainer.default_local_dir=/mnt/general/wanghy/RAGEN_v2/saves/qwen2.5_3B_it_sudoku_withthink_fulltraj_sa_rl # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube1_withthink_sa \ # custom_envs.rubikscube.env_config.scramble_depth=1 \ # trainer.experiment_name=rubikscube1_withthink_sa $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube1_withthink_sas \ # custom_envs.rubikscube.env_config.scramble_depth=1 \ # trainer.experiment_name=rubikscube1_withthink_sas $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube2_withthink_sa \ # custom_envs.rubikscube.env_config.scramble_depth=2 \ # trainer.experiment_name=rubikscube2_withthink_sa $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube2_withthink_sas \ # custom_envs.rubikscube.env_config.scramble_depth=2 \ # trainer.experiment_name=rubikscube2_withthink_sas $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube3_withthink_sa \ # custom_envs.rubikscube.env_config.scramble_depth=3 \ # trainer.experiment_name=rubikscube3_withthink_sa $USE_PPO $USE_BASE # python train.py --config-name _10_rubikscube system.CUDA_VISIBLE_DEVICES="'0,1,2,3,4,5,6,7'" \ # trainer.project_name=ragen_latest_qwen_25_3b_it \ # trainer.n_gpus_per_node=8 \ # model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_rubikscube3_withthink_sas \ # custom_envs.rubikscube.env_config.scramble_depth=3 \ # trainer.experiment_name=rubikscube3_withthink_sas $USE_PPO $USE_BASE