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+_wandb:
+    value:
+        cli_version: 0.23.0
+        code_path: code/cleanrl/cleanrl/ppo_rubikscube.py
+        e:
+            zrvrl6pdc6hzbh0erqwl9hu99oek6ey0:
+                args:
+                    - --track
+                codePath: cleanrl/cleanrl/ppo_rubikscube.py
+                codePathLocal: cleanrl/cleanrl/ppo_rubikscube.py
+                cpu_count: 64
+                cpu_count_logical: 128
+                cudaVersion: "12.4"
+                disk:
+                    /:
+                        total: "5153960755200"
+                        used: "447519772672"
+                email: haoyu-wa22@mails.tsinghua.edu.cn
+                executable: /root/local/miniconda3/envs/ragen/bin/python
+                git:
+                    commit: 8d73639b99b38265453f898b8d6af7d4af50d56e
+                    remote: https://github.com/mll-lab-nu/RAGEN.git
+                gpu: NVIDIA H100 80GB HBM3
+                gpu_count: 8
+                gpu_nvidia:
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-35e2d43d-4067-82ce-90d4-def9e389bf28
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-af4135e3-88f2-e9ac-518d-502c75a85429
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-d7fdeeba-fe9b-ec03-d9f7-6724fe4266b5
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-ccc4f668-3882-5a8e-2c07-c5cd08f6f666
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-7b73c0cf-d3d5-e10c-7176-a43be1e41001
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-81b58d94-5d1f-8ec2-f9d2-fd56172ed177
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-03e8bc66-3b44-6794-49fd-5392fbdda6d1
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-b7cf0ec6-7c29-1179-dceb-09565da51890
+                host: pt-4d654cf4576f4d23ad3d3919f12932fe-worker-0
+                memory:
+                    total: "2163642122240"
+                os: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.35
+                program: /mnt/general/wanghy/RAGEN/cleanrl/cleanrl/ppo_rubikscube.py
+                python: CPython 3.10.19
+                root: /mnt/general/wanghy/RAGEN
+                startedAt: "2025-12-18T10:23:31.950845Z"
+                writerId: zrvrl6pdc6hzbh0erqwl9hu99oek6ey0
+        m:
+            - "1": global_step
+              "6":
+                - 3
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+            - "2": charts/*
+              "5": 1
+              "6":
+                - 1
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+                - 1
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+              "5": 1
+              "6":
+                - 1
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+            - "2": rollout/*
+              "5": 1
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+                - 1
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+              "5": 1
+              "6":
+                - 1
+              "7": []
+            - "2": losses/*
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 11
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+                - 49
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+            "4": 3.10.19
+            "5": 0.23.0
+            "6": 4.57.1
+            "12": 0.23.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 1024
+capture_video:
+    value: false
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+env_id:
+    value: RubiksCube2x2
+eval_episodes:
+    value: 4000
+eval_splits:
+    value: 2
+exp_name:
+    value: ppo_rubikscube
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+max_steps_env:
+    value: 20
+minibatch_size:
+    value: 256
+norm_adv:
+    value: true
+num_envs:
+    value: 8
+num_iterations:
+    value: 976
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+scramble_depth:
+    value: 1
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 1000000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL

wandb/run-20251218_182331-3lp68bhy/files/diff.patch

@@ -0,0 +1,1162 @@
+diff --git a/config/base.yaml b/config/base.yaml
+index a1659e4..9df6250 100644
+--- a/config/base.yaml
++++ b/config/base.yaml
+@@ -9,15 +9,16 @@ seed:
+   train: 10000
+   val: 123
+ 
+-micro_batch_size_per_gpu: 4
++micro_batch_size_per_gpu: 1
+ ppo_mini_batch_size: 32
+-model_path: Qwen/Qwen2.5-3B-Instruct
++model_path: /mnt/general/share/model/Qwen/Qwen2.5-0.5B-Instruct
++# Qwen/Qwen2.5-0.5B-Instruct
+ enable_response_mask: True # Enabling response mask could improve stability of rollout/old_log_prob, as P(st|history) are no longer calculated in loss here. See https://docs.google.com/document/d/1bg7obeiKTExuHHBl5uOiSpec5uLDZ2Tgvxy6li5pHX4/edit?usp=sharing for more details.
+ grpo_advantage_length_weight: False # if you do not enable this and critic/advantage_estimator is GRPO, and the critic/advantages/mean is too low, then you can try enabling this to encourage reasoning and forbid collapse
+ 
+ lora:
+   rank: 0
+-  alpha: 64
++  alpha: 16
+   target_modules: all-linear
+ 
+ actor_rollout_ref:
+@@ -46,10 +47,10 @@ actor_rollout_ref:
+     name: vllm
+     log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu} # following micro_batch_size_per_gpu
+     tensor_model_parallel_size: 1
+-    max_model_len: 3600
++    max_model_len: 7200 #3600 why** 14400
+     prompt_length: 1 # useless. Just put it here
+     response_length: 400 # single-turn response length
+-    gpu_memory_utilization: 0.5
++    gpu_memory_utilization: 0.7
+     max_num_batched_tokens: 8192 # set only when enable_chunked_prefill is true
+     temperature: 1
+     rollout_filter_ratio: 0.25
+@@ -90,26 +91,28 @@ algorithm:
+     kl_coef: 0.000
+ 
+ trainer:
+-  project_name: ragen_latest
++  project_name: 
+   experiment_name: test
+   local_log_dir: "results/"
+-  save_freq: 100
++  save_freq: -1
+   total_training_steps: 200
+   validation_steps: 1 # validation instances = validation_steps * val_env_groups * group_size
+   val_before_train: True
+   n_gpus_per_node: 1
+   test_freq: 10
+   generations_to_log_to_wandb: 
++    train: 128
+     val: 20
+   logger: [ 'console', 'wandb' ]
+   max_actor_ckpt_to_keep: 1
+   max_critic_ckpt_to_keep: 1
++  default_local_dir: /mnt/general/wanghy/RAGEN/saves/
+ 
+ agent_proxy:
+   max_context_window: -1 # set a value > 0 to enable context window for long trajectory
+-  max_turn: 5
++  max_turn: 25 #25 why** 700
+   action_sep: "||"
+-  max_actions_per_turn: 2 # how many actions can be output at most in a single turn
++  max_actions_per_turn: 1 # how many actions can be output at most in a single turn
+   use_turn_scores: False # important to GAE when applying token-level rewards to token-level advantages. If False, will take the sum of scores as the reward for the last turn.
+   enable_think: True # False -> no think RL
+   reward_normalization:
+@@ -126,11 +129,11 @@ es_manager:
+       tags: ["CoordSokoban"]
+       n_groups: [8] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+   val:
+-    env_groups: 32
+-    group_size: 16 # should be set to 1 because when val temperature is set to 0 and group size > 1, there will be repetitive prompts which leads to same trajectory.
++    env_groups: 256
++    group_size: 1 # should be set to 1 because when val temperature is set to 0 and group size > 1, there will be repetitive prompts which leads to same trajectory.
+     env_configs:
+       tags: ["CoordSokoban"]
+-      n_groups: [32] # TODO: If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
++      n_groups: [256] # TODO: If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+ 
+ ctx_manager:
+   generation: # go to vllm
+diff --git a/config/envs.yaml b/config/envs.yaml
+index 1002ac8..ba3ac6f 100644
+--- a/config/envs.yaml
++++ b/config/envs.yaml
+@@ -2,6 +2,7 @@ custom_envs:
+   SimpleSokoban:
+     env_type: sokoban
+     max_actions_per_traj: 10 # used in environment state manager to control the actual max actions executed per trajectory
++    # ORIGNAL env_instruction
+     env_instruction: |
+       You are solving the Sokoban puzzle. 
+       You are the player and you need to push all boxes to targets. 
+@@ -12,7 +13,7 @@ custom_envs:
+     env_config: # keys should be a subset of SokobanConfig
+       dim_x: 6
+       dim_y: 6
+-      num_boxes: 1
++      num_boxes: 2
+       max_steps: 100
+ 
+   LargerSokoban:
+@@ -65,7 +66,7 @@ custom_envs:
+     env_config: # keys should be a subset of SokobanConfig
+       dim_x: 6
+       dim_y: 6
+-      num_boxes: 1
++      num_boxes: 2
+       max_steps: 100
+       observation_format: "grid_coord"
+ 
+@@ -111,14 +112,14 @@ custom_envs:
+ 
+   FrozenLake:
+     env_type: frozen_lake
+-    max_actions_per_traj: 10
++    max_actions_per_traj: 25
+     env_instruction: "You are solving the FrozenLake puzzle. Forbid the whole and go to the target. You may move to the unintended direction due to the slippery ice. Example answer format: <think>To forbid the hole and go to the target, I should go left then go up.</think><answer>Left || Up</answer>"
+     max_tokens: 100
+     env_config: null
+ 
+   CoordFrozenLake:
+     env_type: frozen_lake
+-    max_actions_per_traj: 10
++    max_actions_per_traj: 25
+     env_instruction: |
+       You are solving the FrozenLake puzzle. The observation includes both a symbol grid and zero-indexed coordinates for the start, goal, player, and any holes.
+       Coordinates range from the top-left corner (0, 0) to the bottom-right corner (5, 5).
+@@ -195,4 +196,80 @@ custom_envs:
+     max_actions_per_traj: 30
+     env_instruction: "You are a Lean theorem prover. Given a Lean theorem statement, propose a sequence of tactics that completes the proof. Think step by step about which tactics to apply next. Provide tactics separated by '||', for example <answer>intro || simp || rfl</answer>."
+     max_tokens: 512
+-    env_config: null # Please refer to ragen/env/lean/config.py for a full list of parameters.
+
++    env_config: null # Please refer to ragen/env/lean/config.py for a full list of parameters.
++
++  
++  game_2048:
++    env_type: game_2048
++    max_actions_per_traj: 700
++    env_instruction: |
++      You are playing the 2048 game on a 4x4 grid. Merge equal tiles by sliding Up, Right, Down, or Left.
++      If a move is invalid (no tiles move), a small penalty is applied. Respond with a single action.
++      Example: <answer>Up</answer>
++    max_tokens: 8192
++    env_config: null
++
++  blackjack:
++    env_type: blackjack
++    max_actions_per_traj: 10
++    env_instruction: |
++      You are playing Blackjack against a dealer. The dealer must hit on 16 or less and stand on 17 or more.
++      Choose either Stick or Hit. Respond with a single action.
++      Example: <answer>Hit</answer>
++    max_tokens: 64
++    env_config: null
++
++  rubikscube:
++    env_type: rubikscube
++    max_actions_per_traj: 20
++    env_instruction: |
++      You are solving a 2x2 Rubik's Cube (Pocket Cube). The goal is to restore the cube so that each of the faces consists of a single, unique color.
++      Available actions use standard Singmaster notation for face rotations: U, U', D, D', L, L', R, R', F, F', B, B'.
++      - Faces: U (Up), D (Down), L (Left), R (Right), F (Front), B (Back).
++      - Modifiers: A letter alone means 90° clockwise (e.g., 'R'). A letter with prime (') means 90° counter-clockwise (e.g., "R'").
++      Respond with a sequence of actions separated by "||".
++      Example: <answer>U</answer>
++    max_tokens: 96
++    env_config:
++      scramble_depth: 1
++      max_steps: 20
++      render_mode: "text"
++  
++  SimpleSudoku:
++    env_type: sudoku
++    max_actions_per_traj: 20
++    env_instruction: |
++      You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 2x2 box contains the numbers 1-4 without repetition.
++      Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
++      Place numbers one at a time using the format, for example: <answer>place 1 at row 2 col 3</answer> or <answer>1,2,3</answer>
++      The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
++    max_tokens: 150
++    parallel_friendly: false
++    max_workers: 32
++    env_config:
++      grid_size: 4
++      difficulty: "easy"
++      render_format: "with_feedback"
++      show_conflicts: false
++      show_valid_numbers: false
++      max_steps: 20
++
++  MediumSudoku:
++    env_type: sudoku
++    max_actions_per_traj: 30
++    env_instruction: |
++      You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 3x3 box contains the numbers 1-9 without repetition.
++      Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
++      Place numbers one at a time using the format: <answer>place 5 at row 2 col 3</answer> or <answer>2,3,5</answer>
++      The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
++    max_tokens: 150
++    parallel_friendly: false
++    max_workers: 32
++    env_config:
++      grid_size: 9
++      difficulty: "medium"
++      render_format: "with_feedback"
++      show_conflicts: true
++      show_valid_numbers: true
++      max_steps: 81
++
+diff --git a/config/eval.yaml b/config/eval.yaml
+index e65f3a5..7775fd1 100644
+--- a/config/eval.yaml
++++ b/config/eval.yaml
+@@ -1,14 +1,15 @@
+ defaults:
+   - envs
+-
++enable_response_mask: True
+ system:
+-  CUDA_VISIBLE_DEVICES: "0"
++  CUDA_VISIBLE_DEVICES: "0,1,2,3,4,5,6,7"
+ 
+ seed:
+   train: 10000
+   val: 123
+ 
+-model_path: Qwen/Qwen2.5-3B-Instruct
++model_path: /mnt/general/share/model/openai/gpt-oss-20b
++# /mnt/general/share/model/tyzhu/SPA-frozenlake-qwen2.5-1.5b-instruct
+ 
+ lora:
+   rank: 0
+@@ -24,9 +25,9 @@ actor_rollout_ref:
+   rollout:
+     name: vllm
+     log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+-    tensor_model_parallel_size: 1
++    tensor_model_parallel_size: 8
+     dtype: bfloat16
+-    max_model_len: 3600
++    max_model_len: 7200
+     prompt_length: 1
+     response_length: 400
+     gpu_memory_utilization: 0.9
+@@ -35,6 +36,7 @@ actor_rollout_ref:
+     free_cache_engine: True
+     enable_chunked_prefill: False
+     disable_log_stats: False
++    do_sample: True
+     val_kwargs:
+       do_sample: True
+       temperature: 0.5
+@@ -44,9 +46,9 @@ actor_rollout_ref:
+ 
+ agent_proxy:
+   max_context_window: -1
+-  max_turn: 5
++  max_turn: 25
+   action_sep: "||"
+-  max_actions_per_turn: 2
++  max_actions_per_turn: 1
+   use_turn_scores: False
+   enable_think: True
+   reward_normalization:
+@@ -59,13 +61,13 @@ es_manager:
+     env_groups: 8
+     group_size: 16
+     env_configs:
+-      tags: ["CoordSokoban"]
++      tags: ["BanditTest"]
+       n_groups: [8]
+   val:
+     env_groups: 32
+-    group_size: 16
++    group_size: 128
+     env_configs:
+-      tags: ["CoordSokoban"]
++      tags: ["BanditTest"]
+       n_groups: [32]
+ 
+ ctx_manager:
+diff --git a/config/evaluate_api_llm.yaml b/config/evaluate_api_llm.yaml
+index b21c95e..e2e6761 100644
+--- a/config/evaluate_api_llm.yaml
++++ b/config/evaluate_api_llm.yaml
+@@ -1,8 +1,11 @@
++#export OPENAI_BASE_URL="https://api.ohmygpt.com/v1"
++#export OPENAI_API_KEY="sk-o4sMxBkN5BB100C4D4a3T3BlBkFJF7791CA39EA14ca98041"
++#python -m ragen.eval_api hydra.searchpath='[file://./verl/verl/trainer/config]'
+ defaults:
+   - base # this is a symbolic link to the verl/verl/trainer/config/ppo_trainer.yaml file
+ 
+ model_config:
+-  model_name: gpt-4o # should be registered in model_info
++  model_name: ark-deepseek-v3-250324 # should be registered in model_info
+   max_concurrency: 16
+ 
+ model_info:
+@@ -24,9 +27,9 @@ model_info:
+     generation_kwargs:
+       temperature: 0
+       max_tokens: 512 # max_completion_tokens if o1-mini
+-  gpt-4o:
++  gpt-4o-mini:
+     provider_name: openai
+-    model_name: gpt-4o
++    model_name: gpt-4o-mini
+     generation_kwargs:
+       temperature: 0
+       max_tokens: 512 # max_completion_tokens if o1-mini
+@@ -36,21 +39,42 @@ model_info:
+     generation_kwargs:
+       temperature: 0
+       max_completion_tokens: 512
++  ark-deepseek-v3-250324:
++    provider_name: openai
++    model_name: ark-deepseek-v3-250324
++    generation_kwargs:
++      temperature: 0
++      max_completion_tokens: 512
+   deepseek-v3:
+     provider_name: deepseek
+     model_name: deepseek-chat
+     generation_kwargs:
+       temperature: 0
+       max_completion_tokens: 512
++  glm-4.6:
++    provider_name: openai
++    model_name: glm-4.6
++    generation_kwargs:
++      temperature: 0
++      max_completion_tokens: 512
++  TA/openai/gpt-oss-120b:
++    provider_name: openai
++    model_name: TA/openai/gpt-oss-120b
++    generation_kwargs:
++      temperature: 0
++      max_tokens: 8192
++      # max_retries: 5
+ 
+-
+-
++agent_proxy:
++  max_turn: 5
+ es_manager:
+   val:
+-    env_groups: 256
++    env_groups: 128
+     group_size: 1 # should be set to 1 because val temperature is set to 0 and same prompt leads to same output
+     env_configs:
+-      tags: ["CoordSokoban"]
+-      n_groups: [256] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
++      tags: ["rubikscube"]
++      n_groups: [128] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+ 
+ 
++rollout:
++  max_model_len: 7200
+
+Submodule external/webshop-minimal contains modified content
+diff --git a/external/webshop-minimal/requirements.txt b/external/webshop-minimal/requirements.txt
+index 5a1b04f..238ed5a 100644
+--- a/external/webshop-minimal/requirements.txt
++++ b/external/webshop-minimal/requirements.txt
+@@ -4,7 +4,7 @@ flask
+ html2text
+ rank_bm25
+ pyserini
+-faiss-cpu
++faiss-gpu
+ thefuzz
+ gdown
+ spacy
+diff --git a/ragen/env/__init__.py b/ragen/env/__init__.py
+index b0f3461..bc6838e 100644
+--- a/ragen/env/__init__.py
++++ b/ragen/env/__init__.py
+@@ -12,6 +12,14 @@ from .metamathqa.env import MetaMathQAEnv
+ from .metamathqa.config import MetaMathQAEnvConfig
+ from .lean.config import LeanEnvConfig
+ from .lean.env import LeanEnv
++from .game_2048.config import Game2048EnvConfig
++from .game_2048.env import Game2048Env
++from .blackjack.config import BlackjackEnvConfig
++from .blackjack.env import BlackjackEnv
++from .rubikscube.config import RubiksCube2x2Config
++from .rubikscube.env import RubiksCube2x2Env
++from .sudoku.config import SudokuEnvConfig
++from .sudoku.env import SudokuEnv
+ 
+ 
+ REGISTERED_ENVS = {
+@@ -22,6 +30,10 @@ REGISTERED_ENVS = {
+     # 'alfworld': AlfredTXTEnv,
+     'metamathqa': MetaMathQAEnv,
+     'lean': LeanEnv,
++    'game_2048': Game2048Env,
++    'blackjack': BlackjackEnv,
++    'rubikscube': RubiksCube2x2Env,
++    'sudoku': SudokuEnv,
+ }
+ 
+ REGISTERED_ENV_CONFIGS = {
+@@ -32,6 +44,10 @@ REGISTERED_ENV_CONFIGS = {
+     # 'alfworld': AlfredEnvConfig,
+     'metamathqa': MetaMathQAEnvConfig,
+     'lean': LeanEnvConfig,
++    'game_2048': Game2048EnvConfig,
++    'blackjack': BlackjackEnvConfig,
++    'rubikscube': RubiksCube2x2Config,
++    'sudoku': SudokuEnvConfig,
+ }
+ 
+ try:
+diff --git a/ragen/env/frozen_lake/config.py b/ragen/env/frozen_lake/config.py
+index de054f4..9950c34 100644
+--- a/ragen/env/frozen_lake/config.py
++++ b/ragen/env/frozen_lake/config.py
+@@ -8,7 +8,7 @@ class FrozenLakeEnvConfig:
+     size: int = 4
+     p: float = 0.9
+     success_rate: float = 0.8
+-    is_slippery: bool = True
++    is_slippery: bool = False
+     map_seed: Optional[int] = None
+     render_mode: str = "text"
+     observation_format: str = "grid"
+diff --git a/ragen/env/frozen_lake/env.py b/ragen/env/frozen_lake/env.py
+index 9ee3add..eef11d8 100644
+--- a/ragen/env/frozen_lake/env.py
++++ b/ragen/env/frozen_lake/env.py
+@@ -13,6 +13,7 @@ from ragen.env.base import BaseDiscreteActionEnv
+ class FrozenLakeEnv(BaseDiscreteActionEnv, GymFrozenLakeEnv):
+     def __init__(self, config: FrozenLakeEnvConfig = FrozenLakeEnvConfig()):
+         # Using mappings directly from config
++        # import pdb;pdb.set_trace()
+         self.config = config
+         self.GRID_LOOKUP = config.grid_lookup
+         self.ACTION_LOOKUP = config.action_lookup
+@@ -95,6 +96,7 @@ class FrozenLakeEnv(BaseDiscreteActionEnv, GymFrozenLakeEnv):
+ if __name__ == "__main__":
+     import matplotlib.pyplot as plt
+     config = FrozenLakeEnvConfig(size=4, is_slippery=True)
++    # import pdb;pdb.set_trace()
+     env = FrozenLakeEnv(config)
+     print(env.reset())
+     while True:
+diff --git a/ragen/env/sokoban/env.py b/ragen/env/sokoban/env.py
+index 17cd636..6922b54 100644
+--- a/ragen/env/sokoban/env.py
++++ b/ragen/env/sokoban/env.py
+@@ -33,6 +33,7 @@ class SokobanEnv(BaseDiscreteActionEnv, GymSokobanEnv):
+     def reset(self, seed=None, mode=None):
+         try:
+             with all_seed(seed):
++                # import pdb;pdb.set_trace()
+                 self.room_fixed, self.room_state, self.box_mapping, action_sequence = generate_room(
+                     dim=self.dim_room,
+                     num_steps=self.num_gen_steps,
+diff --git a/ragen/llm_agent/agent_proxy.py b/ragen/llm_agent/agent_proxy.py
+index c25b50d..f6c4088 100644
+--- a/ragen/llm_agent/agent_proxy.py
++++ b/ragen/llm_agent/agent_proxy.py
+@@ -287,6 +287,7 @@ def main(config):
+ 	proxy = LLMAgentProxy(config, actor_wg, tokenizer)
+ 	import time
+ 	start_time = time.time()
++	# import pdb;pdb.set_trace()
+ 	rollouts = proxy.rollout(
+ 		DataProto(
+ 			batch=None,
+diff --git a/ragen/llm_agent/base_llm.py b/ragen/llm_agent/base_llm.py
+index 358a2eb..b6f7797 100644
+--- a/ragen/llm_agent/base_llm.py
++++ b/ragen/llm_agent/base_llm.py
+@@ -33,6 +33,7 @@ class OpenAIProvider(LLMProvider):
+             raise ValueError("OpenAI API key not provided and not found in environment variables")
+         
+         self.client = AsyncOpenAI(api_key=self.api_key)
++        # import pdb;pdb.set_trace()
+     
+     async def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
+         if "o1-mini" in self.model_name:
+@@ -46,10 +47,7 @@ class OpenAIProvider(LLMProvider):
+         )
+         if response.choices[0].finish_reason in ['length', 'content_filter']:
+             raise ValueError("Content filtered or length exceeded")
+-        return LLMResponse(
+-            content=response.choices[0].message.content,
+-            model_name=response.model
+-        )
++        return LLMResponse(content=response.choices[0].message.content,model_name=response.model)
+ 
+ class DeepSeekProvider(LLMProvider):
+     """DeepSeek API provider implementation"""
+@@ -199,7 +197,7 @@ class ConcurrentLLM:
+         
+         # Queue to store unfinished or failed tasks
+         current_batch = messages_list.copy()
+-        max_retries = kwargs.get("max_retries", 100)
++        max_retries = kwargs.get("max_retries", 10)
+         retry_count = 0
+         
+         while current_batch and retry_count < max_retries:
+diff --git a/ragen/llm_agent/ctx_manager.py b/ragen/llm_agent/ctx_manager.py
+index 905247a..af20a01 100644
+--- a/ragen/llm_agent/ctx_manager.py
++++ b/ragen/llm_agent/ctx_manager.py
+@@ -88,6 +88,7 @@ class ContextManager:
+         Initialize the ContextManager.
+         Processor is used to process the image data.
+         """
++        # import pdb;pdb.set_trace()
+         self.config = config
+         self.tokenizer = tokenizer
+         self.processor = processor
+@@ -311,7 +312,7 @@ class ContextManager:
+ 
+             llm_input_texts.append(text_with_prompt)
+             messages_list.append(messages)
+-
++        # import pdb;pdb.set_trace()
+         inputs = self.tokenizer(llm_input_texts, return_tensors="pt", padding=True, padding_side="left", truncation=False) # We have truncated previously, truncation in tokenizer may cause issues.
+         input_ids, attention_mask = inputs.input_ids, inputs.attention_mask
+         position_ids = (attention_mask.cumsum(dim=-1) - 1).clamp(min=0)
+@@ -353,6 +354,30 @@ class ContextManager:
+                 key: np.sum(value) / self.env_nums[key.split("/")[0]]
+                 for key, value in metrics.items()
+             }
++            # Derived metrics for wandb logging
++            try:
++                # charts/avg_episode_return: average across all 2048 env groups
++                ep_keys = [k for k in metrics.keys() if k.endswith('/episodic_return')]
++                if len(ep_keys) > 0:
++                    ep_vals = []
++                    for k in ep_keys:
++                        tag = k.split('/')[0]
++                        denom = self.env_nums.get(tag, max(1, len(metrics[k])))
++                        ep_vals.append(float(np.sum(metrics[k]) / denom))
++                    mean_metrics["charts/avg_episode_return"] = float(np.mean(ep_vals))
++            except Exception:
++                pass
++            try:
++                # rollout/max_tile: max over all envs in this batch
++                tile_keys = [k for k in metrics.keys() if k.endswith('/max_tile')]
++                if len(tile_keys) > 0:
++                    tile_vals = []
++                    for k in tile_keys:
++                        tile_vals.extend(metrics[k])
++                    if len(tile_vals) > 0:
++                        mean_metrics["rollout/max_tile"] = int(np.max(tile_vals))
++            except Exception:
++                pass
+             for key, values in metrics.items():
+                 if not isinstance(values, list):
+                     continue
+diff --git a/ragen/llm_agent/es_manager.py b/ragen/llm_agent/es_manager.py
+index b87a2b3..1a49d3e 100644
+--- a/ragen/llm_agent/es_manager.py
++++ b/ragen/llm_agent/es_manager.py
+@@ -128,18 +128,40 @@ class EnvStateManager:
+         env_outputs: List[Dict]
+             {env_id: int, history: List[Dict][{state: str, actions: List[str], reward: float, info: Dict, llm_response: str, llm_raw_response: str, (Optional)images: List[PIL.Image.Image]}]}
+         """
++        # def _execute_actions(env, actions):
++        #     acc_reward, turn_info, turn_done = 0, {}, False
++        #     executed_actions = []
++        #     for action in actions:
++        #         _, reward, done, info = env.step(action)
++        #         acc_reward += reward
++        #         turn_info.update(info) # NOTE: currently use last info for multi-action
++        #         executed_actions.append(action)
++        #         if done:
++        #             turn_done = True
++        #             break
++            
++        #     return acc_reward, turn_info, turn_done, executed_actions
+         def _execute_actions(env, actions):
+-            acc_reward, turn_info, turn_done = 0, {}, False
++            acc_reward, turn_info, turn_done = 0.0, {}, False
++            raw_acc_reward = 0.0
+             executed_actions = []
+             for action in actions:
+                 _, reward, done, info = env.step(action)
+-                acc_reward += reward
++                acc_reward += float(reward)
++                try:
++                    raw_acc_reward += float(info.get('raw_reward', 0.0))
++                except Exception:
++                    pass
+                 turn_info.update(info) # NOTE: currently use last info for multi-action
+                 executed_actions.append(action)
+                 if done:
+                     turn_done = True
+                     break
+-            
++            # Overwrite per-turn raw_reward to reflect the sum across all executed actions in this turn
++            try:
++                turn_info['raw_reward'] = float(raw_acc_reward)
++            except Exception:
++                pass
+             return acc_reward, turn_info, turn_done, executed_actions
+ 
+         def _log_env_state(status, history, cur_obs, max_actions_per_traj, executed_actions, all_actions, acc_reward, turn_done, turn_info, env_input):
+@@ -198,6 +220,18 @@ class EnvStateManager:
+                 'success': float(status.terminated and (not status.truncated)),
+                 'num_actions': status.num_actions,
+             }
++            # Add episodic-level metrics
++            # try:
++            #     # Sum of per-turn rewards equals the episodic return (env-shaped reward)
++            #     env_metric['episodic_return'] = float(sum(status.rewards))
++            # except Exception:
++            #     pass
++            try:
++                # Final max tile on the board at the end of the rollout
++                import numpy as _np
++                env_metric['max_tile'] = int(_np.max(entry['env'].grid))
++            except Exception:
++                pass
+             custom_metric = {}
+             for turn in cache['history']:
+                 for k, v in turn.get('info', {}).items():
+@@ -212,6 +246,12 @@ class EnvStateManager:
+                             "Skipping non-numeric metric '%s' with value %r for env %s.",
+                             k, v, entry['tag']
+                         )
++            # Add episodic_return as the SUM of raw_reward across steps (align with CleanRL)
++            try:
++                if 'raw_reward' in custom_metric:
++                    env_metric['episodic_return'] = float(np.sum(custom_metric['raw_reward']))
++            except Exception:
++                pass
+             for k, v in custom_metric.items():
+                 # TODO: Move TURN_LVL_METRICS into the environment
+                 if "webshop" not in cache['tag'].lower() or ("webshop" in cache['tag'].lower() and k in TURN_LVL_METRICS):
+@@ -219,7 +259,12 @@ class EnvStateManager:
+                 else:
+                     env_metric['traj_sum/' + k] = np.sum(v)
+ 
+-
++            try:
++                if 'score' in custom_metric and len(custom_metric['score']) > 0:
++                    env_metric['final_score'] = float(custom_metric['score'][-1])
++            except Exception:
++                pass
++                
+             cache['history'][-1]['metrics'] = custom_metric
+             env_metric = {f"{entry['tag']}/{k}": v for k, v in env_metric.items()}
+             cache['metrics'] = env_metric
+diff --git a/requirements.txt b/requirements.txt
+index 2fd756e..9b521bf 100644
+--- a/requirements.txt
++++ b/requirements.txt
+@@ -7,7 +7,6 @@ accelerate
+ codetiming
+ datasets
+ dill
+-flash-attn==2.7.4.post1
+ hydra-core
+ numpy
+ pandas
+@@ -15,19 +14,19 @@ pybind11
+ ray>=2.10
+ tensordict>=0.8.0,<0.9.0
+ transformers
+-vllm==0.8.2
++vllm==0.8.5
+ wandb
+ gymnasium
+ gymnasium[toy-text]
+ 
+ pyarrow>=15.0.0
+ pylatexenc
+-torchdata
++# torchdata
+ debugpy
+ 
+ together
+ anthropic
+-faiss-cpu==1.11.0
++faiss-gpu
+ 
+ # This is optional, but needs to be installed with main requirements if you want to use webshop
+ -r external/webshop-minimal/requirements.txt
+diff --git a/scripts/setup_ragen.sh b/scripts/setup_ragen.sh
+index f9a7cd9..85a93c4 100644
+--- a/scripts/setup_ragen.sh
++++ b/scripts/setup_ragen.sh
+@@ -94,10 +94,10 @@ main() {
+         pip install torch==2.5.0 --index-url https://download.pytorch.org/whl/cu124
+         
+         print_step "Installing flash-attention..."
+-        pip3 install flash-attn==2.7.4.post1 --no-build-isolation
++        # pip3 install flash-attn==2.7.4.post1 --no-build-isolation
+     else
+         print_step "Installing PyTorch without CUDA support..."
+-        pip install torch==2.5.0
++        pip install torch==2.4.0
+     fi
+     
+     # Install remaining requirements
+@@ -137,8 +137,8 @@ main() {
+     conda install conda-forge::gdown
+     mkdir -p external/webshop-minimal/webshop_minimal/data/full
+     cd external/webshop-minimal/webshop_minimal/data/full
+-    gdown https://drive.google.com/uc?id=1A2whVgOO0euk5O13n2iYDM0bQRkkRduB # items_shuffle
+-    gdown https://drive.google.com/uc?id=1s2j6NgHljiZzQNL3veZaAiyW_qDEgBNi # items_ins_v2
++    # gdown https://drive.google.com/uc?id=1A2whVgOO0euk5O13n2iYDM0bQRkkRduB # items_shuffle
++    # gdown https://drive.google.com/uc?id=1s2j6NgHljiZzQNL3veZaAiyW_qDEgBNi # items_ins_v2
+     cd ../../../../..
+ 
+     echo -e "${GREEN}Installation completed successfully!${NC}"
+diff --git a/train_all.sh b/train_all.sh
+index 0157306..33035f5 100755
+--- a/train_all.sh
++++ b/train_all.sh
+@@ -6,246 +6,257 @@ USE_GRPO="algorithm.adv_estimator=grpo"
+ 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="0" trainer.experiment_name=bandit-ppo $USE_PPO $USE_BASE &
+-python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=bandit-grpo $USE_GRPO $USE_BASE &
+-python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=sokoban-ppo $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="4" trainer.experiment_name=frozen_lake-ppo $USE_PPO $USE_BASE &
+-python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=frozen_lake-grpo $USE_GRPO $USE_BASE &
++# 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 
+ 
+-# 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 &
++# 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 
+ 
+-wait
++# 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 5.1 - Reasoning Helps Generalization
++# # 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 &
+ 
+-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"
++# wait
+ 
+-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
++# # 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 &
+ 
+-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"
++# wait
+ 
+-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
++# # 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
+ 
+-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 &
++# # 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"
+ 
+-# 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 &
++# 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"
+ 
+-# 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 &
++# 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
+ 
+-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 &
++# 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 &
+ 
+-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 &
++# # 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 &
+ 
+-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 &
++# # 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
+ 
+-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 &
+ 
+-# 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 &
++# wait
+ 
+-# 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] &
++# 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 
++# 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=\"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 &
+ 
+-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 &
++# 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 &
+ 
+-# 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 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 -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 
++# 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 &
+ 
+-# 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 &
+ 
++# # 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_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 &
++# # 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 _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 &
++# 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 &
+ 
+-# 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  
++# # 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 &
+ 
+-# 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
+ 
++# 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 &
+ 
+-# 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 &
++# # 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 &
+ 
+-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 &
++# # 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  
+ 
+-# 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 &
++# # 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
+ 
+ 
+-# 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 &
++# # 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 _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 &
++# 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 _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 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 _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 &
++# # 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 \
++#     trainer.experiment_name=3b-full-ppo-test system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2
+

wandb/run-20251218_182331-3lp68bhy/files/diff_8d73639b99b38265453f898b8d6af7d4af50d56e.patch

@@ -0,0 +1,1162 @@
+diff --git a/config/base.yaml b/config/base.yaml
+index a1659e4..9df6250 100644
+--- a/config/base.yaml
++++ b/config/base.yaml
+@@ -9,15 +9,16 @@ seed:
+   train: 10000
+   val: 123
+ 
+-micro_batch_size_per_gpu: 4
++micro_batch_size_per_gpu: 1
+ ppo_mini_batch_size: 32
+-model_path: Qwen/Qwen2.5-3B-Instruct
++model_path: /mnt/general/share/model/Qwen/Qwen2.5-0.5B-Instruct
++# Qwen/Qwen2.5-0.5B-Instruct
+ enable_response_mask: True # Enabling response mask could improve stability of rollout/old_log_prob, as P(st|history) are no longer calculated in loss here. See https://docs.google.com/document/d/1bg7obeiKTExuHHBl5uOiSpec5uLDZ2Tgvxy6li5pHX4/edit?usp=sharing for more details.
+ grpo_advantage_length_weight: False # if you do not enable this and critic/advantage_estimator is GRPO, and the critic/advantages/mean is too low, then you can try enabling this to encourage reasoning and forbid collapse
+ 
+ lora:
+   rank: 0
+-  alpha: 64
++  alpha: 16
+   target_modules: all-linear
+ 
+ actor_rollout_ref:
+@@ -46,10 +47,10 @@ actor_rollout_ref:
+     name: vllm
+     log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu} # following micro_batch_size_per_gpu
+     tensor_model_parallel_size: 1
+-    max_model_len: 3600
++    max_model_len: 7200 #3600 why** 14400
+     prompt_length: 1 # useless. Just put it here
+     response_length: 400 # single-turn response length
+-    gpu_memory_utilization: 0.5
++    gpu_memory_utilization: 0.7
+     max_num_batched_tokens: 8192 # set only when enable_chunked_prefill is true
+     temperature: 1
+     rollout_filter_ratio: 0.25
+@@ -90,26 +91,28 @@ algorithm:
+     kl_coef: 0.000
+ 
+ trainer:
+-  project_name: ragen_latest
++  project_name: 
+   experiment_name: test
+   local_log_dir: "results/"
+-  save_freq: 100
++  save_freq: -1
+   total_training_steps: 200
+   validation_steps: 1 # validation instances = validation_steps * val_env_groups * group_size
+   val_before_train: True
+   n_gpus_per_node: 1
+   test_freq: 10
+   generations_to_log_to_wandb: 
++    train: 128
+     val: 20
+   logger: [ 'console', 'wandb' ]
+   max_actor_ckpt_to_keep: 1
+   max_critic_ckpt_to_keep: 1
++  default_local_dir: /mnt/general/wanghy/RAGEN/saves/
+ 
+ agent_proxy:
+   max_context_window: -1 # set a value > 0 to enable context window for long trajectory
+-  max_turn: 5
++  max_turn: 25 #25 why** 700
+   action_sep: "||"
+-  max_actions_per_turn: 2 # how many actions can be output at most in a single turn
++  max_actions_per_turn: 1 # how many actions can be output at most in a single turn
+   use_turn_scores: False # important to GAE when applying token-level rewards to token-level advantages. If False, will take the sum of scores as the reward for the last turn.
+   enable_think: True # False -> no think RL
+   reward_normalization:
+@@ -126,11 +129,11 @@ es_manager:
+       tags: ["CoordSokoban"]
+       n_groups: [8] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+   val:
+-    env_groups: 32
+-    group_size: 16 # should be set to 1 because when val temperature is set to 0 and group size > 1, there will be repetitive prompts which leads to same trajectory.
++    env_groups: 256
++    group_size: 1 # should be set to 1 because when val temperature is set to 0 and group size > 1, there will be repetitive prompts which leads to same trajectory.
+     env_configs:
+       tags: ["CoordSokoban"]
+-      n_groups: [32] # TODO: If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
++      n_groups: [256] # TODO: If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+ 
+ ctx_manager:
+   generation: # go to vllm
+diff --git a/config/envs.yaml b/config/envs.yaml
+index 1002ac8..ba3ac6f 100644
+--- a/config/envs.yaml
++++ b/config/envs.yaml
+@@ -2,6 +2,7 @@ custom_envs:
+   SimpleSokoban:
+     env_type: sokoban
+     max_actions_per_traj: 10 # used in environment state manager to control the actual max actions executed per trajectory
++    # ORIGNAL env_instruction
+     env_instruction: |
+       You are solving the Sokoban puzzle. 
+       You are the player and you need to push all boxes to targets. 
+@@ -12,7 +13,7 @@ custom_envs:
+     env_config: # keys should be a subset of SokobanConfig
+       dim_x: 6
+       dim_y: 6
+-      num_boxes: 1
++      num_boxes: 2
+       max_steps: 100
+ 
+   LargerSokoban:
+@@ -65,7 +66,7 @@ custom_envs:
+     env_config: # keys should be a subset of SokobanConfig
+       dim_x: 6
+       dim_y: 6
+-      num_boxes: 1
++      num_boxes: 2
+       max_steps: 100
+       observation_format: "grid_coord"
+ 
+@@ -111,14 +112,14 @@ custom_envs:
+ 
+   FrozenLake:
+     env_type: frozen_lake
+-    max_actions_per_traj: 10
++    max_actions_per_traj: 25
+     env_instruction: "You are solving the FrozenLake puzzle. Forbid the whole and go to the target. You may move to the unintended direction due to the slippery ice. Example answer format: <think>To forbid the hole and go to the target, I should go left then go up.</think><answer>Left || Up</answer>"
+     max_tokens: 100
+     env_config: null
+ 
+   CoordFrozenLake:
+     env_type: frozen_lake
+-    max_actions_per_traj: 10
++    max_actions_per_traj: 25
+     env_instruction: |
+       You are solving the FrozenLake puzzle. The observation includes both a symbol grid and zero-indexed coordinates for the start, goal, player, and any holes.
+       Coordinates range from the top-left corner (0, 0) to the bottom-right corner (5, 5).
+@@ -195,4 +196,80 @@ custom_envs:
+     max_actions_per_traj: 30
+     env_instruction: "You are a Lean theorem prover. Given a Lean theorem statement, propose a sequence of tactics that completes the proof. Think step by step about which tactics to apply next. Provide tactics separated by '||', for example <answer>intro || simp || rfl</answer>."
+     max_tokens: 512
+-    env_config: null # Please refer to ragen/env/lean/config.py for a full list of parameters.
+
++    env_config: null # Please refer to ragen/env/lean/config.py for a full list of parameters.
++
++  
++  game_2048:
++    env_type: game_2048
++    max_actions_per_traj: 700
++    env_instruction: |
++      You are playing the 2048 game on a 4x4 grid. Merge equal tiles by sliding Up, Right, Down, or Left.
++      If a move is invalid (no tiles move), a small penalty is applied. Respond with a single action.
++      Example: <answer>Up</answer>
++    max_tokens: 8192
++    env_config: null
++
++  blackjack:
++    env_type: blackjack
++    max_actions_per_traj: 10
++    env_instruction: |
++      You are playing Blackjack against a dealer. The dealer must hit on 16 or less and stand on 17 or more.
++      Choose either Stick or Hit. Respond with a single action.
++      Example: <answer>Hit</answer>
++    max_tokens: 64
++    env_config: null
++
++  rubikscube:
++    env_type: rubikscube
++    max_actions_per_traj: 20
++    env_instruction: |
++      You are solving a 2x2 Rubik's Cube (Pocket Cube). The goal is to restore the cube so that each of the faces consists of a single, unique color.
++      Available actions use standard Singmaster notation for face rotations: U, U', D, D', L, L', R, R', F, F', B, B'.
++      - Faces: U (Up), D (Down), L (Left), R (Right), F (Front), B (Back).
++      - Modifiers: A letter alone means 90° clockwise (e.g., 'R'). A letter with prime (') means 90° counter-clockwise (e.g., "R'").
++      Respond with a sequence of actions separated by "||".
++      Example: <answer>U</answer>
++    max_tokens: 96
++    env_config:
++      scramble_depth: 1
++      max_steps: 20
++      render_mode: "text"
++  
++  SimpleSudoku:
++    env_type: sudoku
++    max_actions_per_traj: 20
++    env_instruction: |
++      You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 2x2 box contains the numbers 1-4 without repetition.
++      Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
++      Place numbers one at a time using the format, for example: <answer>place 1 at row 2 col 3</answer> or <answer>1,2,3</answer>
++      The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
++    max_tokens: 150
++    parallel_friendly: false
++    max_workers: 32
++    env_config:
++      grid_size: 4
++      difficulty: "easy"
++      render_format: "with_feedback"
++      show_conflicts: false
++      show_valid_numbers: false
++      max_steps: 20
++
++  MediumSudoku:
++    env_type: sudoku
++    max_actions_per_traj: 30
++    env_instruction: |
++      You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 3x3 box contains the numbers 1-9 without repetition.
++      Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
++      Place numbers one at a time using the format: <answer>place 5 at row 2 col 3</answer> or <answer>2,3,5</answer>
++      The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
++    max_tokens: 150
++    parallel_friendly: false
++    max_workers: 32
++    env_config:
++      grid_size: 9
++      difficulty: "medium"
++      render_format: "with_feedback"
++      show_conflicts: true
++      show_valid_numbers: true
++      max_steps: 81
++
+diff --git a/config/eval.yaml b/config/eval.yaml
+index e65f3a5..7775fd1 100644
+--- a/config/eval.yaml
++++ b/config/eval.yaml
+@@ -1,14 +1,15 @@
+ defaults:
+   - envs
+-
++enable_response_mask: True
+ system:
+-  CUDA_VISIBLE_DEVICES: "0"
++  CUDA_VISIBLE_DEVICES: "0,1,2,3,4,5,6,7"
+ 
+ seed:
+   train: 10000
+   val: 123
+ 
+-model_path: Qwen/Qwen2.5-3B-Instruct
++model_path: /mnt/general/share/model/openai/gpt-oss-20b
++# /mnt/general/share/model/tyzhu/SPA-frozenlake-qwen2.5-1.5b-instruct
+ 
+ lora:
+   rank: 0
+@@ -24,9 +25,9 @@ actor_rollout_ref:
+   rollout:
+     name: vllm
+     log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+-    tensor_model_parallel_size: 1
++    tensor_model_parallel_size: 8
+     dtype: bfloat16
+-    max_model_len: 3600
++    max_model_len: 7200
+     prompt_length: 1
+     response_length: 400
+     gpu_memory_utilization: 0.9
+@@ -35,6 +36,7 @@ actor_rollout_ref:
+     free_cache_engine: True
+     enable_chunked_prefill: False
+     disable_log_stats: False
++    do_sample: True
+     val_kwargs:
+       do_sample: True
+       temperature: 0.5
+@@ -44,9 +46,9 @@ actor_rollout_ref:
+ 
+ agent_proxy:
+   max_context_window: -1
+-  max_turn: 5
++  max_turn: 25
+   action_sep: "||"
+-  max_actions_per_turn: 2
++  max_actions_per_turn: 1
+   use_turn_scores: False
+   enable_think: True
+   reward_normalization:
+@@ -59,13 +61,13 @@ es_manager:
+     env_groups: 8
+     group_size: 16
+     env_configs:
+-      tags: ["CoordSokoban"]
++      tags: ["BanditTest"]
+       n_groups: [8]
+   val:
+     env_groups: 32
+-    group_size: 16
++    group_size: 128
+     env_configs:
+-      tags: ["CoordSokoban"]
++      tags: ["BanditTest"]
+       n_groups: [32]
+ 
+ ctx_manager:
+diff --git a/config/evaluate_api_llm.yaml b/config/evaluate_api_llm.yaml
+index b21c95e..e2e6761 100644
+--- a/config/evaluate_api_llm.yaml
++++ b/config/evaluate_api_llm.yaml
+@@ -1,8 +1,11 @@
++#export OPENAI_BASE_URL="https://api.ohmygpt.com/v1"
++#export OPENAI_API_KEY="sk-o4sMxBkN5BB100C4D4a3T3BlBkFJF7791CA39EA14ca98041"
++#python -m ragen.eval_api hydra.searchpath='[file://./verl/verl/trainer/config]'
+ defaults:
+   - base # this is a symbolic link to the verl/verl/trainer/config/ppo_trainer.yaml file
+ 
+ model_config:
+-  model_name: gpt-4o # should be registered in model_info
++  model_name: ark-deepseek-v3-250324 # should be registered in model_info
+   max_concurrency: 16
+ 
+ model_info:
+@@ -24,9 +27,9 @@ model_info:
+     generation_kwargs:
+       temperature: 0
+       max_tokens: 512 # max_completion_tokens if o1-mini
+-  gpt-4o:
++  gpt-4o-mini:
+     provider_name: openai
+-    model_name: gpt-4o
++    model_name: gpt-4o-mini
+     generation_kwargs:
+       temperature: 0
+       max_tokens: 512 # max_completion_tokens if o1-mini
+@@ -36,21 +39,42 @@ model_info:
+     generation_kwargs:
+       temperature: 0
+       max_completion_tokens: 512
++  ark-deepseek-v3-250324:
++    provider_name: openai
++    model_name: ark-deepseek-v3-250324
++    generation_kwargs:
++      temperature: 0
++      max_completion_tokens: 512
+   deepseek-v3:
+     provider_name: deepseek
+     model_name: deepseek-chat
+     generation_kwargs:
+       temperature: 0
+       max_completion_tokens: 512
++  glm-4.6:
++    provider_name: openai
++    model_name: glm-4.6
++    generation_kwargs:
++      temperature: 0
++      max_completion_tokens: 512
++  TA/openai/gpt-oss-120b:
++    provider_name: openai
++    model_name: TA/openai/gpt-oss-120b
++    generation_kwargs:
++      temperature: 0
++      max_tokens: 8192
++      # max_retries: 5
+ 
+-
+-
++agent_proxy:
++  max_turn: 5
+ es_manager:
+   val:
+-    env_groups: 256
++    env_groups: 128
+     group_size: 1 # should be set to 1 because val temperature is set to 0 and same prompt leads to same output
+     env_configs:
+-      tags: ["CoordSokoban"]
+-      n_groups: [256] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
++      tags: ["rubikscube"]
++      n_groups: [128] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation
+ 
+ 
++rollout:
++  max_model_len: 7200
+
+Submodule external/webshop-minimal contains modified content
+diff --git a/external/webshop-minimal/requirements.txt b/external/webshop-minimal/requirements.txt
+index 5a1b04f..238ed5a 100644
+--- a/external/webshop-minimal/requirements.txt
++++ b/external/webshop-minimal/requirements.txt
+@@ -4,7 +4,7 @@ flask
+ html2text
+ rank_bm25
+ pyserini
+-faiss-cpu
++faiss-gpu
+ thefuzz
+ gdown
+ spacy
+diff --git a/ragen/env/__init__.py b/ragen/env/__init__.py
+index b0f3461..bc6838e 100644
+--- a/ragen/env/__init__.py
++++ b/ragen/env/__init__.py
+@@ -12,6 +12,14 @@ from .metamathqa.env import MetaMathQAEnv
+ from .metamathqa.config import MetaMathQAEnvConfig
+ from .lean.config import LeanEnvConfig
+ from .lean.env import LeanEnv
++from .game_2048.config import Game2048EnvConfig
++from .game_2048.env import Game2048Env
++from .blackjack.config import BlackjackEnvConfig
++from .blackjack.env import BlackjackEnv
++from .rubikscube.config import RubiksCube2x2Config
++from .rubikscube.env import RubiksCube2x2Env
++from .sudoku.config import SudokuEnvConfig
++from .sudoku.env import SudokuEnv
+ 
+ 
+ REGISTERED_ENVS = {
+@@ -22,6 +30,10 @@ REGISTERED_ENVS = {
+     # 'alfworld': AlfredTXTEnv,
+     'metamathqa': MetaMathQAEnv,
+     'lean': LeanEnv,
++    'game_2048': Game2048Env,
++    'blackjack': BlackjackEnv,
++    'rubikscube': RubiksCube2x2Env,
++    'sudoku': SudokuEnv,
+ }
+ 
+ REGISTERED_ENV_CONFIGS = {
+@@ -32,6 +44,10 @@ REGISTERED_ENV_CONFIGS = {
+     # 'alfworld': AlfredEnvConfig,
+     'metamathqa': MetaMathQAEnvConfig,
+     'lean': LeanEnvConfig,
++    'game_2048': Game2048EnvConfig,
++    'blackjack': BlackjackEnvConfig,
++    'rubikscube': RubiksCube2x2Config,
++    'sudoku': SudokuEnvConfig,
+ }
+ 
+ try:
+diff --git a/ragen/env/frozen_lake/config.py b/ragen/env/frozen_lake/config.py
+index de054f4..9950c34 100644
+--- a/ragen/env/frozen_lake/config.py
++++ b/ragen/env/frozen_lake/config.py
+@@ -8,7 +8,7 @@ class FrozenLakeEnvConfig:
+     size: int = 4
+     p: float = 0.9
+     success_rate: float = 0.8
+-    is_slippery: bool = True
++    is_slippery: bool = False
+     map_seed: Optional[int] = None
+     render_mode: str = "text"
+     observation_format: str = "grid"
+diff --git a/ragen/env/frozen_lake/env.py b/ragen/env/frozen_lake/env.py
+index 9ee3add..eef11d8 100644
+--- a/ragen/env/frozen_lake/env.py
++++ b/ragen/env/frozen_lake/env.py
+@@ -13,6 +13,7 @@ from ragen.env.base import BaseDiscreteActionEnv
+ class FrozenLakeEnv(BaseDiscreteActionEnv, GymFrozenLakeEnv):
+     def __init__(self, config: FrozenLakeEnvConfig = FrozenLakeEnvConfig()):
+         # Using mappings directly from config
++        # import pdb;pdb.set_trace()
+         self.config = config
+         self.GRID_LOOKUP = config.grid_lookup
+         self.ACTION_LOOKUP = config.action_lookup
+@@ -95,6 +96,7 @@ class FrozenLakeEnv(BaseDiscreteActionEnv, GymFrozenLakeEnv):
+ if __name__ == "__main__":
+     import matplotlib.pyplot as plt
+     config = FrozenLakeEnvConfig(size=4, is_slippery=True)
++    # import pdb;pdb.set_trace()
+     env = FrozenLakeEnv(config)
+     print(env.reset())
+     while True:
+diff --git a/ragen/env/sokoban/env.py b/ragen/env/sokoban/env.py
+index 17cd636..6922b54 100644
+--- a/ragen/env/sokoban/env.py
++++ b/ragen/env/sokoban/env.py
+@@ -33,6 +33,7 @@ class SokobanEnv(BaseDiscreteActionEnv, GymSokobanEnv):
+     def reset(self, seed=None, mode=None):
+         try:
+             with all_seed(seed):
++                # import pdb;pdb.set_trace()
+                 self.room_fixed, self.room_state, self.box_mapping, action_sequence = generate_room(
+                     dim=self.dim_room,
+                     num_steps=self.num_gen_steps,
+diff --git a/ragen/llm_agent/agent_proxy.py b/ragen/llm_agent/agent_proxy.py
+index c25b50d..f6c4088 100644
+--- a/ragen/llm_agent/agent_proxy.py
++++ b/ragen/llm_agent/agent_proxy.py
+@@ -287,6 +287,7 @@ def main(config):
+ 	proxy = LLMAgentProxy(config, actor_wg, tokenizer)
+ 	import time
+ 	start_time = time.time()
++	# import pdb;pdb.set_trace()
+ 	rollouts = proxy.rollout(
+ 		DataProto(
+ 			batch=None,
+diff --git a/ragen/llm_agent/base_llm.py b/ragen/llm_agent/base_llm.py
+index 358a2eb..b6f7797 100644
+--- a/ragen/llm_agent/base_llm.py
++++ b/ragen/llm_agent/base_llm.py
+@@ -33,6 +33,7 @@ class OpenAIProvider(LLMProvider):
+             raise ValueError("OpenAI API key not provided and not found in environment variables")
+         
+         self.client = AsyncOpenAI(api_key=self.api_key)
++        # import pdb;pdb.set_trace()
+     
+     async def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
+         if "o1-mini" in self.model_name:
+@@ -46,10 +47,7 @@ class OpenAIProvider(LLMProvider):
+         )
+         if response.choices[0].finish_reason in ['length', 'content_filter']:
+             raise ValueError("Content filtered or length exceeded")
+-        return LLMResponse(
+-            content=response.choices[0].message.content,
+-            model_name=response.model
+-        )
++        return LLMResponse(content=response.choices[0].message.content,model_name=response.model)
+ 
+ class DeepSeekProvider(LLMProvider):
+     """DeepSeek API provider implementation"""
+@@ -199,7 +197,7 @@ class ConcurrentLLM:
+         
+         # Queue to store unfinished or failed tasks
+         current_batch = messages_list.copy()
+-        max_retries = kwargs.get("max_retries", 100)
++        max_retries = kwargs.get("max_retries", 10)
+         retry_count = 0
+         
+         while current_batch and retry_count < max_retries:
+diff --git a/ragen/llm_agent/ctx_manager.py b/ragen/llm_agent/ctx_manager.py
+index 905247a..af20a01 100644
+--- a/ragen/llm_agent/ctx_manager.py
++++ b/ragen/llm_agent/ctx_manager.py
+@@ -88,6 +88,7 @@ class ContextManager:
+         Initialize the ContextManager.
+         Processor is used to process the image data.
+         """
++        # import pdb;pdb.set_trace()
+         self.config = config
+         self.tokenizer = tokenizer
+         self.processor = processor
+@@ -311,7 +312,7 @@ class ContextManager:
+ 
+             llm_input_texts.append(text_with_prompt)
+             messages_list.append(messages)
+-
++        # import pdb;pdb.set_trace()
+         inputs = self.tokenizer(llm_input_texts, return_tensors="pt", padding=True, padding_side="left", truncation=False) # We have truncated previously, truncation in tokenizer may cause issues.
+         input_ids, attention_mask = inputs.input_ids, inputs.attention_mask
+         position_ids = (attention_mask.cumsum(dim=-1) - 1).clamp(min=0)
+@@ -353,6 +354,30 @@ class ContextManager:
+                 key: np.sum(value) / self.env_nums[key.split("/")[0]]
+                 for key, value in metrics.items()
+             }
++            # Derived metrics for wandb logging
++            try:
++                # charts/avg_episode_return: average across all 2048 env groups
++                ep_keys = [k for k in metrics.keys() if k.endswith('/episodic_return')]
++                if len(ep_keys) > 0:
++                    ep_vals = []
++                    for k in ep_keys:
++                        tag = k.split('/')[0]
++                        denom = self.env_nums.get(tag, max(1, len(metrics[k])))
++                        ep_vals.append(float(np.sum(metrics[k]) / denom))
++                    mean_metrics["charts/avg_episode_return"] = float(np.mean(ep_vals))
++            except Exception:
++                pass
++            try:
++                # rollout/max_tile: max over all envs in this batch
++                tile_keys = [k for k in metrics.keys() if k.endswith('/max_tile')]
++                if len(tile_keys) > 0:
++                    tile_vals = []
++                    for k in tile_keys:
++                        tile_vals.extend(metrics[k])
++                    if len(tile_vals) > 0:
++                        mean_metrics["rollout/max_tile"] = int(np.max(tile_vals))
++            except Exception:
++                pass
+             for key, values in metrics.items():
+                 if not isinstance(values, list):
+                     continue
+diff --git a/ragen/llm_agent/es_manager.py b/ragen/llm_agent/es_manager.py
+index b87a2b3..1a49d3e 100644
+--- a/ragen/llm_agent/es_manager.py
++++ b/ragen/llm_agent/es_manager.py
+@@ -128,18 +128,40 @@ class EnvStateManager:
+         env_outputs: List[Dict]
+             {env_id: int, history: List[Dict][{state: str, actions: List[str], reward: float, info: Dict, llm_response: str, llm_raw_response: str, (Optional)images: List[PIL.Image.Image]}]}
+         """
++        # def _execute_actions(env, actions):
++        #     acc_reward, turn_info, turn_done = 0, {}, False
++        #     executed_actions = []
++        #     for action in actions:
++        #         _, reward, done, info = env.step(action)
++        #         acc_reward += reward
++        #         turn_info.update(info) # NOTE: currently use last info for multi-action
++        #         executed_actions.append(action)
++        #         if done:
++        #             turn_done = True
++        #             break
++            
++        #     return acc_reward, turn_info, turn_done, executed_actions
+         def _execute_actions(env, actions):
+-            acc_reward, turn_info, turn_done = 0, {}, False
++            acc_reward, turn_info, turn_done = 0.0, {}, False
++            raw_acc_reward = 0.0
+             executed_actions = []
+             for action in actions:
+                 _, reward, done, info = env.step(action)
+-                acc_reward += reward
++                acc_reward += float(reward)
++                try:
++                    raw_acc_reward += float(info.get('raw_reward', 0.0))
++                except Exception:
++                    pass
+                 turn_info.update(info) # NOTE: currently use last info for multi-action
+                 executed_actions.append(action)
+                 if done:
+                     turn_done = True
+                     break
+-            
++            # Overwrite per-turn raw_reward to reflect the sum across all executed actions in this turn
++            try:
++                turn_info['raw_reward'] = float(raw_acc_reward)
++            except Exception:
++                pass
+             return acc_reward, turn_info, turn_done, executed_actions
+ 
+         def _log_env_state(status, history, cur_obs, max_actions_per_traj, executed_actions, all_actions, acc_reward, turn_done, turn_info, env_input):
+@@ -198,6 +220,18 @@ class EnvStateManager:
+                 'success': float(status.terminated and (not status.truncated)),
+                 'num_actions': status.num_actions,
+             }
++            # Add episodic-level metrics
++            # try:
++            #     # Sum of per-turn rewards equals the episodic return (env-shaped reward)
++            #     env_metric['episodic_return'] = float(sum(status.rewards))
++            # except Exception:
++            #     pass
++            try:
++                # Final max tile on the board at the end of the rollout
++                import numpy as _np
++                env_metric['max_tile'] = int(_np.max(entry['env'].grid))
++            except Exception:
++                pass
+             custom_metric = {}
+             for turn in cache['history']:
+                 for k, v in turn.get('info', {}).items():
+@@ -212,6 +246,12 @@ class EnvStateManager:
+                             "Skipping non-numeric metric '%s' with value %r for env %s.",
+                             k, v, entry['tag']
+                         )
++            # Add episodic_return as the SUM of raw_reward across steps (align with CleanRL)
++            try:
++                if 'raw_reward' in custom_metric:
++                    env_metric['episodic_return'] = float(np.sum(custom_metric['raw_reward']))
++            except Exception:
++                pass
+             for k, v in custom_metric.items():
+                 # TODO: Move TURN_LVL_METRICS into the environment
+                 if "webshop" not in cache['tag'].lower() or ("webshop" in cache['tag'].lower() and k in TURN_LVL_METRICS):
+@@ -219,7 +259,12 @@ class EnvStateManager:
+                 else:
+                     env_metric['traj_sum/' + k] = np.sum(v)
+ 
+-
++            try:
++                if 'score' in custom_metric and len(custom_metric['score']) > 0:
++                    env_metric['final_score'] = float(custom_metric['score'][-1])
++            except Exception:
++                pass
++                
+             cache['history'][-1]['metrics'] = custom_metric
+             env_metric = {f"{entry['tag']}/{k}": v for k, v in env_metric.items()}
+             cache['metrics'] = env_metric
+diff --git a/requirements.txt b/requirements.txt
+index 2fd756e..9b521bf 100644
+--- a/requirements.txt
++++ b/requirements.txt
+@@ -7,7 +7,6 @@ accelerate
+ codetiming
+ datasets
+ dill
+-flash-attn==2.7.4.post1
+ hydra-core
+ numpy
+ pandas
+@@ -15,19 +14,19 @@ pybind11
+ ray>=2.10
+ tensordict>=0.8.0,<0.9.0
+ transformers
+-vllm==0.8.2
++vllm==0.8.5
+ wandb
+ gymnasium
+ gymnasium[toy-text]
+ 
+ pyarrow>=15.0.0
+ pylatexenc
+-torchdata
++# torchdata
+ debugpy
+ 
+ together
+ anthropic
+-faiss-cpu==1.11.0
++faiss-gpu
+ 
+ # This is optional, but needs to be installed with main requirements if you want to use webshop
+ -r external/webshop-minimal/requirements.txt
+diff --git a/scripts/setup_ragen.sh b/scripts/setup_ragen.sh
+index f9a7cd9..85a93c4 100644
+--- a/scripts/setup_ragen.sh
++++ b/scripts/setup_ragen.sh
+@@ -94,10 +94,10 @@ main() {
+         pip install torch==2.5.0 --index-url https://download.pytorch.org/whl/cu124
+         
+         print_step "Installing flash-attention..."
+-        pip3 install flash-attn==2.7.4.post1 --no-build-isolation
++        # pip3 install flash-attn==2.7.4.post1 --no-build-isolation
+     else
+         print_step "Installing PyTorch without CUDA support..."
+-        pip install torch==2.5.0
++        pip install torch==2.4.0
+     fi
+     
+     # Install remaining requirements
+@@ -137,8 +137,8 @@ main() {
+     conda install conda-forge::gdown
+     mkdir -p external/webshop-minimal/webshop_minimal/data/full
+     cd external/webshop-minimal/webshop_minimal/data/full
+-    gdown https://drive.google.com/uc?id=1A2whVgOO0euk5O13n2iYDM0bQRkkRduB # items_shuffle
+-    gdown https://drive.google.com/uc?id=1s2j6NgHljiZzQNL3veZaAiyW_qDEgBNi # items_ins_v2
++    # gdown https://drive.google.com/uc?id=1A2whVgOO0euk5O13n2iYDM0bQRkkRduB # items_shuffle
++    # gdown https://drive.google.com/uc?id=1s2j6NgHljiZzQNL3veZaAiyW_qDEgBNi # items_ins_v2
+     cd ../../../../..
+ 
+     echo -e "${GREEN}Installation completed successfully!${NC}"
+diff --git a/train_all.sh b/train_all.sh
+index 0157306..33035f5 100755
+--- a/train_all.sh
++++ b/train_all.sh
+@@ -6,246 +6,257 @@ USE_GRPO="algorithm.adv_estimator=grpo"
+ 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="0" trainer.experiment_name=bandit-ppo $USE_PPO $USE_BASE &
+-python train.py --config-name _1_bandit system.CUDA_VISIBLE_DEVICES="1" trainer.experiment_name=bandit-grpo $USE_GRPO $USE_BASE &
+-python train.py --config-name _2_sokoban system.CUDA_VISIBLE_DEVICES="2" trainer.experiment_name=sokoban-ppo $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="4" trainer.experiment_name=frozen_lake-ppo $USE_PPO $USE_BASE &
+-python train.py --config-name _3_frozen_lake system.CUDA_VISIBLE_DEVICES="5" trainer.experiment_name=frozen_lake-grpo $USE_GRPO $USE_BASE &
++# 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 
+ 
+-# 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 &
++# 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 
+ 
+-wait
++# 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 5.1 - Reasoning Helps Generalization
++# # 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 &
+ 
+-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"
++# wait
+ 
+-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
++# # 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 &
+ 
+-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"
++# wait
+ 
+-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
++# # 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
+ 
+-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 &
++# # 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"
+ 
+-# 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 &
++# 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"
+ 
+-# 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 &
++# 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
+ 
+-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 &
++# 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 &
+ 
+-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 &
++# # 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 &
+ 
+-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 &
++# # 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
+ 
+-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 &
+ 
+-# 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 &
++# wait
+ 
+-# 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] &
++# 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 
++# 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=\"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 &
+ 
+-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 &
++# 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 &
+ 
+-# 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 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 -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 
++# 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 &
+ 
+-# 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 &
+ 
++# # 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_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 &
++# # 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 _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 &
++# 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 &
+ 
+-# 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  
++# # 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 &
+ 
+-# 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
+ 
++# 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 &
+ 
+-# 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 &
++# # 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 &
+ 
+-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 &
++# # 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  
+ 
+-# 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 &
++# # 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
+ 
+ 
+-# 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 &
++# # 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 _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 &
++# 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 _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 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 _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 &
++# # 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 \
++#     trainer.experiment_name=3b-full-ppo-test system.CUDA_VISIBLE_DEVICES=\"0,1\" trainer.n_gpus_per_node=2
+

wandb/run-20251218_182331-3lp68bhy/run-3lp68bhy.wandb

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