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config/_2_sokoban.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+ 
+trainer:
+  experiment_name: sokoban-main

config/_4_countdown.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+
+trainer:
+  experiment_name: countdown
+
+
+agent_proxy:
+  max_turn: 1
+  max_actions_per_turn: 1
+
+es_manager:
+  train:
+    env_configs:
+      tags: ["Countdown"]
+  val:
+    env_configs:
+      tags: ["Countdown"]

config/_6_webshop.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+
+micro_batch_size_per_gpu: 4
+ppo_mini_batch_size: 32
+model_path: Qwen/Qwen2.5-3B-Instruct
+
+trainer:
+  experiment_name: webshop
+
+
+agent_proxy:
+  max_turn: 9
+  max_actions_per_turn: 1
+
+actor_rollout_ref:
+  rollout:
+    max_model_len: 15000
+    max_num_batched_tokens: 15000
+
+es_manager:
+  train:
+    env_configs:
+      tags: ["WebShop"]
+  val:
+    env_configs:
+      tags: ["WebShop"]

config/_7_lean.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+
+system:
+  CUDA_VISIBLE_DEVICES: "0,1,2,3"
+
+trainer:
+  experiment_name: lean
+  n_gpus_per_node: 4
+
+agent_proxy:
+  max_turn: 15
+  max_actions_per_turn: 4
+  max_context_window: 5
+
+actor_rollout_ref:
+  rollout:
+    max_model_len: 8096
+    response_length: 512
+
+es_manager:
+  train:
+    env_configs:
+      tags: ["Lean"]
+  val:
+    env_configs:
+      tags: ["Lean"]

config/_8_sudoku.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+
+trainer:
+  experiment_name: sudoku-main
+
+es_manager:
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags: ["SimpleSudoku"]
+      n_groups: [8]
+  val:
+    env_groups: 32
+    group_size: 16
+    env_configs:
+      tags: ["SimpleSudoku"]
+      n_groups: [32]

config/base.yaml

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+defaults:
+  - ppo_trainer
+  - envs
+
+system:
+  CUDA_VISIBLE_DEVICES: "0,1,2,3,4,5,6,7"
+
+seed:
+  train: 10000
+  val: 123
+
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: 
+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
+  target_modules: all-linear
+
+actor_rollout_ref:
+  model:
+    path: ${model_path}
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  actor:
+    ppo_mini_batch_size: ${ppo_mini_batch_size}  # by default, ppo_mini_batch_size = train_batch_size / 4
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_ref: True
+    entropy_coeff: 0.001
+    use_kl_loss: False
+    kl_loss_coef: 0.000
+    kl_loss_type: kl
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: "none" # "none", "linear", "sqrt"
+    # Loss aggregation mode: "token-mean" (default), "seq-mean-token-mean" (GRPO), "seq-mean-token-sum" (Dr. GRPO)
+    loss_agg_mode: "token-mean"
+    optim:
+      betas: [0.9, 0.999]
+      lr: 1e-6
+  ref:
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+  rollout:
+    name: vllm
+    load_format: auto  # load from huggingface instead of dummy (random init)
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    tensor_model_parallel_size: 1
+    max_model_len: 16384
+    prompt_length: 1 # useless. Just put it here
+    response_length: 400 # single-turn response length
+    gpu_memory_utilization: 0.7
+    max_num_batched_tokens: 16384 # set only when enable_chunked_prefill is true
+    temperature: 1
+    rollout_filter_value: 1.0
+    rollout_filter_strategy: top_p # top_p, top_k, top_k_abs, min_p
+    rollout_filter_type: largest # smallest or largest
+    rollout_filter_include_zero: True # whether to include groups with 0 score in the filtering
+    rollout_filter_top_p_prob_mode: linear # top_p mode: score-sum linear rule or original softmax
+    rollout_filter_selection_eps: 0.01 # linear top_p uses threshold = top_p * sum(scores) - eps
+    rollout_filter_empty_stop_steps: 5 # early stop after this many consecutive training steps with 0 kept samples
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+    enforce_eager: True #  for small models, set both enforce_eager and free_cache_engine to False to make rollout faster
+    free_cache_engine: True
+    val_kwargs:
+      do_sample: True
+      temperature: 0.5
+
+critic:
+  ppo_mini_batch_size: ${ppo_mini_batch_size} # by default, ppo_mini_batch_size = train_batch_size / 4
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  model:
+    path: ${model_path}
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  optim:
+    betas: [0.9, 0.999]
+    lr: 1e-5
+
+data:
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+
+algorithm:
+  gamma: 1.0
+  lam: 1.0
+  high_level_gamma: 0.95
+  adv_estimator: gae  # "gae" for PPO, "grpo" for GRPO/Dr.GRPO
+  bi_level_gae: False
+  zero_task_advantage: False  # if True, zero out advantages to remove task-driven policy gradient
+  # Dr. GRPO: set to False to use (R - mean) instead of (R - mean) / std
+  norm_adv_by_std_in_grpo: True
+  # Soft advantage reweighting: scale advantages by (group_std / max_group_std)
+  # This down-weights low reward variance prompts instead of hard filtering
+  soft_advantage_reweight: False
+  kl_penalty: kl  # how to estimate kl divergence
+  kl_ctrl:
+    type: fixed
+    kl_coef: 0.000
+
+trainer:
+  project_name: ragen
+  experiment_name: test
+  local_log_dir: "results/"
+  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: 8
+  test_freq: 10
+  generations_to_log_to_wandb:
+    val: 20
+  logger: [ 'console', 'wandb' ]
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  log_group_rv_table: False
+  gradient_analysis_mode: False
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: False
+  gradient_analysis_only: False
+  exit_after_gradient_analysis: False
+
+agent_proxy:
+  context_window_mode: "full" # "full" | "limited_multi_turn" | "single_turn"
+  max_context_window: -1 # k value: -1 for full history, 1 for no history (like without_history)
+  batch_adjust_mode: copy # "copy" to duplicate samples, "delete" to remove samples when batch size is not divisible
+  max_turn: 10
+  action_sep: "||"
+  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:
+    grouping: "state" # state / batch / inductive
+    method: "identity" # asym_clip / identity / mean_std
+
+# Collapse detection for diagnosing template collapse vs entropy collapse
+collapse_detection:
+  compute_freq: 5  # Compute every N steps
+  micro_batch_size: 128  # Micro batch size for cross-scoring
+  first_turn_enabled: true  # Compute first-turn metrics
+  multi_turn_enabled: true  # Enable multi-turn sampling for MI computation
+  num_samples: 64  # N or all
+
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    # under the same group, the env config and env seed are ensured to be equal
+    group_size: 16
+    env_configs:
+      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: 512
+    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: [512] # 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
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

config/stream.yaml

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+defaults:
+  - base
+
+hydra:
+  searchpath:
+    - pkg://verl.trainer.config
+
+trainer:
+  experiment_name: sokoban-main
+
+
+es_manager:
+  val:
+    env_groups: 1
+    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: ["SimpleSokoban"]
+      n_groups: [1] # 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

config/webshop_full.yaml

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+defaults:
+  - base
+
+micro_batch_size_per_gpu: 4
+ppo_mini_batch_size: 32
+model_path: Qwen/Qwen2.5-3B-Instruct
+
+trainer:
+  experiment_name: webshop_full
+
+
+agent_proxy:
+  max_turn: 15
+  max_actions_per_turn: 1
+
+actor_rollout_ref:
+  rollout:
+    max_model_len: 15000
+    max_num_batched_tokens: 15000
+
+es_manager:
+  train:
+    env_configs:
+      tags: ["WebShopFull"]
+  val:
+    env_configs:
+      tags: ["WebShopFull"]

docs/experiment_deepcoder.md

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+# DeepCoder Experiment Runs
+
+## Command Snippets Overview
+
+| Condition | Purpose | Variables |
+|--------|---------|-----------|
+| `GRPO top-p 1.0` | Full-retention baseline under linear top-p filtering | `rollout_filter_strategy=top_p`, `rollout_filter_value=1`, `rollout_filter_include_zero=True` |
+| `GRPO top-p 0.9` | Stronger reward-variance filtering with adaptive retention | `rollout_filter_strategy=top_p`, `rollout_filter_value=0.9`, `rollout_filter_include_zero=False` |
+| `GRPO top-k 0.25` | Fixed-budget filtering that keeps the top 25% of train groups | `rollout_filter_strategy=top_k`, `rollout_filter_value=0.25`, `rollout_filter_include_zero=True` |
+
+All three command snippets run DeepCoder with `Qwen/Qwen2.5-Coder-7B`, `GRPO`, and single-turn code generation.
+
+---
+
+## 1. Top-p 1.0 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-1`)
+
+Uses linear top-p filtering with `rollout_filter_value=1`.
+
+Goal:
+- Establish a full-retention baseline while keeping the same reward-variance ranking machinery as the filtered runs
+
+Key Details:
+- Filtering uses `top_p`, `largest`, `reward_variance`, and `rollout_filter_top_p_prob_mode=linear`
+- `rollout_filter_value=1` with `rollout_filter_include_zero=True` keeps the full train-group pool under linear top-p selection, so this is the closest thing to a no-filter baseline in `deepcoder_lines`
+- `actor_rollout_ref.actor.use_ref=False` and `actor_rollout_ref.actor.use_kl_loss=False` remove reference-policy KL from training
+- The run budget is `200` training steps with checkpoints every `20` steps
+
+Source:
+- `docs/deepcoder_lines`, lines `1-89`
+
+Outputs:
+- W&B project: `deepcoder_RAGEN_final_3`
+- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-1`
+
+---
+
+## 2. Top-p 0.9 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-0.9`)
+
+Uses the same linear top-p filter, but keeps only the highest-variance groups whose score mass reaches `0.9`.
+
+Goal:
+- Increase reward-variance filtering strength while keeping the rest of the GRPO setup fixed
+
+Key Details:
+- Filtering again uses `top_p`, `largest`, `reward_variance`, and `rollout_filter_top_p_prob_mode=linear`
+- `rollout_filter_value=0.9` makes retention adaptive: the number of kept groups depends on how reward variance is distributed across the `16` train groups
+- `rollout_filter_include_zero=False` excludes zero-variance groups from selection
+- Because `rollout_filter_type=largest`, the filter prioritizes groups with the highest within-group reward variance
+
+Source:
+- `docs/deepcoder_lines`, lines `93-181`
+
+Outputs:
+- W&B project: `deepcoder_RAGEN_final_3`
+- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-0.9`
+
+---
+
+## 3. Top-k 0.25 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-k-0.25`)
+
+Switches from adaptive top-p filtering to fixed-fraction top-k filtering.
+
+Goal:
+- Compare adaptive top-p filtering against a fixed keep-top-25% regime
+
+Key Details:
+- `rollout_filter_strategy=top_k` with `rollout_filter_value=0.25` keeps `int(0.25 * 16) = 4` train groups per step
+- With `es_manager.train.group_size=8`, this corresponds to at most `32` kept rollouts per training step after filtering
+- `rollout_filter_include_zero=True` means zero-variance groups are still part of the ranking pool, but only the top `4` groups survive
+- `rollout_filter_type=largest` means those `4` groups are chosen by highest reward variance
+
+Source:
+- `docs/deepcoder_lines`, lines `185-273`
+
+Outputs:
+- W&B project: `deepcoder_RAGEN_final_3`
+- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-k-0.25`
+
+---
+
+## Common Notes
+
+- Source format:
+  - `docs/deepcoder_lines` is a collection of three standalone bash snippets, not a parameterized sweep script
+  - The file defines both `USE_GRPO` and `USE_PPO`, but all three `python train.py` commands actually expand `$USE_GRPO`
+- Shared setup across all three conditions:
+  - Config: `_10_deepcoder`
+  - Model: `Qwen/Qwen2.5-Coder-7B`
+  - `algorithm.adv_estimator=grpo`
+  - `agent_proxy.reward_normalization.method=identity`
+  - `trainer.total_training_steps=200`
+  - `ppo_mini_batch_size=32`
+  - `micro_batch_size_per_gpu=1`
+  - `es_manager.train.env_groups=16`, `es_manager.train.group_size=8`
+  - `es_manager.val.env_groups=256`, `es_manager.val.group_size=1`
+  - `trainer.n_gpus_per_node=8`
+  - `system.CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"`
+  - `actor_rollout_ref.rollout.tensor_model_parallel_size=4`
+  - `agent_proxy.max_turn=1`
+  - `actor_rollout_ref.actor.use_ref=False`
+  - `actor_rollout_ref.rollout.rollout_filter_type=largest`
+  - `actor_rollout_ref.rollout.rollout_filter_metric=reward_variance` by default from `config/base.yaml`
+  - `actor_rollout_ref.rollout.rollout_filter_top_p_prob_mode=linear`
+  - `actor_rollout_ref.rollout.rollout_filter_empty_stop_steps=0`
+  - `actor_rollout_ref.rollout.max_model_len=10000`
+  - `actor_rollout_ref.rollout.max_num_batched_tokens=10000`
+  - `actor_rollout_ref.rollout.response_length=4000`
+  - `agent_proxy.fail_on_prompt_too_long=True`
+  - `lora.rank=0`, `lora.alpha=64`, `lora.target_modules=all-linear`
+  - `actor_rollout_ref.rollout.gpu_memory_utilization=0.6`
+  - `trainer.save_freq=20`
+  - `trainer.validation_steps=1`
+  - `trainer.val_before_train=True`
+  - `trainer.test_freq=10`
+  - `collapse_detection.first_turn_enabled=False`
+  - `collapse_detection.multi_turn_enabled=False`
+  - `trainer.resume_mode=disable`
+- Logging and artifacts:
+  - Default local log dir remains `results/`
+  - Default logger remains `['console', 'wandb']`
+  - Checkpoints are saved every `20` steps

docs/experiment_main_table.md

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+# Main Table Runs
+
+This doc covers the experiment scripts for the main performance table.
+
+## Scripts Overview
+
+| Script | Purpose | Variables |
+|--------|---------|-----------|
+| `run_main_table_diff_algo.sh` | Compare algorithms | PPO, DAPO, GRPO, DrGRPO; `filter`/`nofilter` |
+| `run_main_table_diff_size.sh` | Compare model sizes | 0.5B, 1.5B, 3B, 7B; `filter`/`nofilter` |
+| `run_main_table_diff_model.sh` | Compare model types | Instruct, Reasoning; `filter`/`nofilter` |
+
+All scripts run experiments across 5 tasks (sokoban, frozenlake, webshop, metamathqa, countdown) with filter/nofilter settings.
+
+---
+
+## 1. Different Algorithms (`run_main_table_diff_algo.sh`)
+
+Compares PPO/DAPO/GRPO/DrGRPO using Qwen2.5-3B.
+
+```bash
+bash scripts/runs/run_main_table_diff_algo.sh --steps 400
+```
+
+Options:
+- `--steps` (default: `400`)
+- `--tasks` (comma list; default: `sokoban,frozenlake,webshop,metamathqa,countdown`)
+- `--algos` (comma list; default: `PPO,DAPO,GRPO,DrGRPO`)
+- `--gpus` (comma list; auto-detect if omitted)
+- `--gpus-per-exp` (default: `1`)
+- `--cooldown` (seconds; default: `30`)
+- `--gpu-memory-utilization` (default: `0.3`)
+- `--filters` (comma list; `filter`, `nofilter`, or `all`; default: `all`)
+
+Examples:
+```bash
+# Run one `filter` and one `nofilter` PPO experiment on 4xH100 each
+bash scripts/runs/run_main_table_diff_algo.sh --steps 400 --tasks sokoban --gpus-per-exp 4 --gpu-memory-utilization 0.3 --filters all --gpus 0,1,2,3,4,5,6,7 --algos PPO
+```
+
+Outputs:
+- Per-task logs: `logs/diff_algo_<task>_Qwen2.5-3B/`
+- Summary log: `logs/diff_algo_Qwen2.5-3B.log`
+
+---
+
+## 2. Different Model Sizes (`run_main_table_diff_size.sh`)
+
+Compares Qwen2.5 models of different sizes using PPO.
+
+```bash
+bash scripts/runs/run_main_table_diff_size.sh --steps 400
+```
+
+Options:
+- `--steps` (default: `400`)
+- `--tasks` (comma list; default: `sokoban,frozenlake,webshop,metamathqa,countdown`)
+- `--models` (comma list; default: `Qwen2.5-0.5B,Qwen2.5-1.5B,Qwen2.5-3B,Qwen2.5-7B`)
+- `--gpus` (comma list; auto-detect if omitted)
+- `--gpus-per-exp` (default: `1`)
+- `--cooldown` (seconds; default: `30`)
+- `--gpu-memory-utilization` (default: `0.3`)
+- `--filters` (comma list; `filter`, `nofilter`, or `all`; default: `all`)
+
+Examples:
+```bash
+# Run a single 1.5B/filter experiment on 4xH100
+bash scripts/runs/run_main_table_diff_size.sh --steps 400 --tasks sokoban --gpus-per-exp 4 --gpu-memory-utilization 0.3 --filters filter --gpus 0,1,2,3 --models Qwen2.5-1.5B
+
+# Quick test with smallest model
+bash scripts/runs/run_main_table_diff_size.sh --steps 5 --models Qwen2.5-0.5B --tasks sokoban
+```
+
+Outputs:
+- Per-task logs: `logs/diff_size_<task>/`
+- Summary log: `logs/diff_size_PPO.log`
+
+---
+
+## 3. Different Model Types (`run_main_table_diff_model.sh`)
+
+Compares different model types (Instruct, Reasoning) using PPO.
+
+```bash
+bash scripts/runs/run_main_table_diff_model.sh --steps 400
+```
+
+Options:
+- `--steps` (default: `400`)
+- `--tasks` (comma list; default: `sokoban,frozenlake,webshop,metamathqa,countdown`)
+- `--models` (comma list; default: `Qwen2.5-3B-Instruct`)
+- `--gpus` (comma list; auto-detect if omitted)
+- `--gpus-per-exp` (default: `1`)
+- `--cooldown` (seconds; default: `30`)
+- `--gpu-memory-utilization` (default: `0.3`)
+- `--filters` (comma list; `filter`, `nofilter`, or `all`; default: `all`)
+
+Examples:
+```bash
+# Run one `filter` and one `nofilter` Llama-3.2-3B-Instruct experiment on 4xH100 each
+bash scripts/runs/run_main_table_diff_model.sh --steps 400 --tasks sokoban --gpus-per-exp 4 --gpu-memory-utilization 0.5 --filters all --gpus 0,1,2,3,4,5,6,7 --models=meta-llama/Llama-3.2-3B-Instruct
+```
+
+Outputs:
+- Per-task logs: `logs/diff_model_<task>/`
+- Summary log: `logs/diff_model_PPO.log`
+
+---
+
+## Common Notes
+
+- Effective rollout filter config for main-table runs:
+  - `rollout_filter_strategy=top_p`
+  - `rollout_filter_top_p_prob_mode=softmax`
+  - `rollout_filter_type=largest`
+  - `rollout_filter_metric=reward_variance`
+  - `rollout_filter_include_zero=True`
+- Filter mode mapping:
+  - `filter`: `top_p=0.9`, `include_zero=True`
+  - `nofilter`: `top_p=1.0`, `include_zero=True`
+- Because `include_zero=True`, `nofilter` (`top_p=1.0`) keeps all groups; it does not disable the filter code path, but it is effectively "no filtering" for the batch
+- You can run a single experiment on `4xH100` by setting `--gpus-per-exp 4` and passing a 4-GPU list, or run one `filter` and one `nofilter` experiment in parallel by passing an 8-GPU list

docs/experiment_search.md

@@ -0,0 +1,163 @@
+# Search Environment Experiments
+
+This doc covers the experiment scripts for the Search (HotpotQA + Dense Retrieval) environment.
+
+## Overview
+
+All experiments use:
+- **Task**: SearchQA (HotpotQA multi-hop QA with Wikipedia dense retrieval)
+- **Model**: `Qwen/Qwen2.5-3B-Instruct`
+- **Algorithm**: PPO (`algorithm.adv_estimator=gae`)
+- **Config**: `_9_search`
+
+The sweep compares three rollout filtering strategies while keeping all other hyperparameters fixed.
+
+| Experiment | Filter Strategy | Filter Value | Effective Batch | Description |
+|-----------|----------------|-------------|----------------|-------------|
+| No Filter | `top_p` | `1.0` | 128 | Baseline: all rollout groups kept |
+| TopK 0.25 | `top_k` | `0.25` | 32 | Keep top 25% groups by reward variance |
+| TopP 0.9 | `top_p` | `0.9` | ~115 | Keep groups covering 90% cumulative reward variance |
+
+---
+
+## Prerequisites
+
+### 1. Prepare data
+
+```bash
+# HotpotQA train/val parquet
+python scripts/prepare_search_data.py
+
+# Wikipedia corpus + FAISS index (~74GB)
+python scripts/download_search_index.py
+```
+
+### 2. Start retrieval server
+
+The retrieval server provides dense retrieval over ~21M Wikipedia passages using E5-base-v2 + FAISS.
+
+```bash
+python scripts/retrieval/server.py \
+    --data_dir ./search_data/prebuilt_indices \
+    --port 8000 --host 127.0.0.1 \
+    --device cuda:0 --gpu_memory_limit_mb 6144
+```
+
+**Important**: We recommend running the retrieval server on a **dedicated GPU** not used by training, or on CPU. Sharing a GPU with vLLM rollout and training causes CUDA OOM errors due to memory contention between processes.
+
+---
+
+## Experiment Scripts
+
+All experiments use `scripts/runs/run_search_benchmark.sh`.
+
+### Experiment 1: PPO + No Filter (baseline)
+
+No filtering — all rollout groups are used for training.
+
+```bash
+bash scripts/runs/run_search_benchmark.sh \
+    --algos PPO \
+    --filter-strategy top_p --filter-value 1.0 \
+    --gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
+    --micro-batch 4 --mini-batch 64 \
+    --gpu-memory-utilization 0.65 \
+    --save-freq 20 --steps 200 \
+    --retrieval-port 8000
+```
+
+### Experiment 2: PPO + TopK=0.25
+
+Keep only the top 25% of rollout groups ranked by reward variance.
+
+```bash
+bash scripts/runs/run_search_benchmark.sh \
+    --algos PPO \
+    --filter-strategy top_k --filter-value 0.25 \
+    --gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
+    --micro-batch 4 --mini-batch 32 \
+    --gpu-memory-utilization 0.65 \
+    --save-freq 20 --steps 200 \
+    --retrieval-port 8000
+```
+
+Note: `mini-batch` is reduced to 32 because effective batch after filtering is `16 groups * 8 group_size * 0.25 = 32`. The `ppo_mini_batch_size` must not exceed this value.
+
+### Experiment 3: PPO + TopP=0.9
+
+Keep rollout groups covering the top 90% cumulative reward variance (softmax-weighted).
+
+```bash
+bash scripts/runs/run_search_benchmark.sh \
+    --algos PPO \
+    --filter-strategy top_p --filter-value 0.9 \
+    --gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
+    --micro-batch 4 --mini-batch 64 \
+    --gpu-memory-utilization 0.65 \
+    --save-freq 20 --steps 200 \
+    --retrieval-port 8000
+```
+
+---
+
+## W&B Runs
+
+Project: [`cuhksz-gc/ragen_search_benchmark`](https://wandb.ai/cuhksz-gc/ragen_search_benchmark)
+
+| Experiment | Run ID | Link |
+|-----------|--------|------|
+| PPO + No Filter | `2sbt8952` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/2sbt8952) |
+| PPO + TopK=0.25 | `2h5c7kbb` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/2h5c7kbb) |
+| PPO + TopP=0.9 | `tbgx0lpt` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/tbgx0lpt) |
+
+---
+
+## Shared Config
+
+```yaml
+# config/_9_search.yaml overrides
+micro_batch_size_per_gpu: 4
+ppo_mini_batch_size: 32-64  # depends on filter setting
+
+agent_proxy:
+  max_turn: 5
+  max_actions_per_turn: 1
+
+actor_rollout_ref:
+  rollout:
+    max_model_len: 5000       # TopK=0.25 experiment used 4000
+    max_num_batched_tokens: 5000  # TopK=0.25 experiment used 4000
+    gpu_memory_utilization: 0.65
+    temperature: 1
+  actor:
+    use_kl_loss: False
+    kl_loss_coef: 0.001
+    entropy_coeff: 0.001
+    loss_agg_mode: token-mean
+    filter_loss_scaling: none
+
+es_manager:
+  train:
+    env_groups: 16
+    group_size: 8    # 16 * 8 = 128 rollouts per step
+  val:
+    env_groups: 256
+
+collapse_detection:
+  compute_freq: 999  # effectively disabled
+
+trainer:
+  total_training_steps: 200
+  save_freq: 20
+  val_before_train: True
+  logger: ['console', 'wandb']
+```
+
+---
+
+## Common Notes
+
+- **Retrieval server GPU deployment**: Place the E5 retrieval server on a **dedicated GPU** not used by training. Co-locating with training on the same GPU causes CUDA OOM due to memory contention between vLLM, training, and the E5 server process. Do not use CPU mode — during rollout, hundreds of environments issue concurrent retrieval requests (256 env groups can produce 1000+ requests), and CPU cannot keep up.
+- **mini-batch size adjustment**: When using aggressive filtering (e.g., `top_k=0.25`), reduce `ppo_mini_batch_size` so it does not exceed `env_groups * group_size * filter_value`. Otherwise training fails with an assertion error.
+- **max_model_len**: Default is 5000 (in `_9_search.yaml`). The TopK=0.25 experiment used 4000 to save KV cache memory; the No Filter and TopP=0.9 experiments use the default 5000.
+- **Checkpoint size**: Each checkpoint is ~35GB (model + optimizer, 8 FSDP shards). With `save_freq=20` and 200 steps, expect 10 checkpoints (~350GB). Monitor disk usage and delete old checkpoints as needed.

docs/guide_filtering_and_loss_scaling.md

@@ -0,0 +1,157 @@
+# Filtering Strategies and Loss Scaling in RAGEN
+
+## Overview
+This document details the advanced filtering strategies and the loss scaling mechanism implemented to stabilize Reinforcement Learning (RL) training, particularly when using aggressive filtering techniques in the GRPO/PPO loop.
+
+Note: the short guide for the current `top_p`, `top_k`, and no-filter variants lives in [guide_rollout_filtering.md](./guide_rollout_filtering.md).
+
+## 1. Rolling Filter Strategies (`rollout_filter_strategy`)
+We have implemented three strategies to filter rollout groups based on their rewards/scores.
+
+### `top_p` (Nucleus Sampling)
+-   **Description**: Selects the smallest set of groups whose **cumulative probability** (derived from the softmax of scores) exceeds the threshold `value`.
+-   **Configuration**:
+    ```yaml
+    actor_rollout_ref:
+      rollout:
+        rollout_filter_strategy: top_p
+        rollout_filter_value: 0.5  # Keep top cumulative 50% probability mass
+    ```
+-   **Behavior**:
+    -   Scores are converted to logits (negated if `rollout_filter_type: smallest`).
+    -   Softmax is applied to get probabilities.
+    -   Groups are sorted by probability.
+    -   Groups are selected until the cumulative sum $\ge$ `value`.
+    -   **Constraint**: Always keeps at least one group.
+
+### `top_k`
+-   **Description**: Selects the top fraction `value` (e.g., 0.5 for 50%) of groups.
+-   **Configuration**:
+    ```yaml
+    actor_rollout_ref:
+      rollout:
+        rollout_filter_strategy: top_k
+        rollout_filter_value: 0.5  # Keep top 50% groups
+    ```
+
+### `top_k_abs`
+-   **Description**: Selects specifically the top `k` groups with the highest (or lowest) scores.
+-   **Configuration**:
+    ```yaml
+    actor_rollout_ref:
+      rollout:
+        rollout_filter_strategy: top_k_abs
+        rollout_filter_value: 4  # Keep top 4 groups
+    ```
+-   **Behavior**: A simple sorting and slicing operation. Useful for guaranteeing a fixed batch size of "good" examples.
+
+
+### `min_p`
+-   **Description**: Selects groups whose score is at least a fraction `value` of the maximum score in the batch.
+-   **Behavior**:
+    -   **`largest`**: Keeps groups where $\text{score} \ge \text{max\_score} \cdot \text{value}$.
+    -   **`smallest`**: Keeps groups where $\text{score} \le \text{min\_score} / \text{value}$.
+-   **Configuration**:
+    ```yaml
+    actor_rollout_ref:
+      rollout:
+        rollout_filter_strategy: min_p
+        rollout_filter_value: 0.8  # Keep groups with score >= 0.8 * max_score
+    ```
+
+### Other Parameters
+-   **`rollout_filter_metric`**: `reward_variance` (default), `reward`, `reward_sum`, `entropy`, `entropy_variance`, or `length`.
+-   **`rollout_filter_type`**: `largest` (default) or `smallest`. Determines if we want high or low scores.
+-   **`rollout_filter_include_zero`**: If `True`, groups with zero score are candidates for filtering. If `False`, they are excluded or handled differently depending on the specific logic (often used to ensure we don't train on complete failures).
+
+---
+
+## 2. Filter Loss Scaling (`filter_loss_scaling`)
+Aggressive filtering (e.g., `top_p=0.2`) can result in keeping only a small fraction of the generated prompts. This can lead to high variance in gradients. To mitigate this, we implemented loss scaling.
+
+### Concept
+We scale the PPO policy loss (and potentially KL/entropy components depending on the implementation) by a factor derived from the **kept ratio**:
+$$ \text{ratio} = \frac{N_{\text{kept}}}{N_{\text{total}}} $$
+
+### Configuration
+Controlled via `actor_rollout_ref.actor.filter_loss_scaling`:
+
+1.  **`none`** (Default): No scaling.
+    $$ \mathcal{L}_{\text{final}} = \mathcal{L}_{\text{ppo}} $$
+
+2.  **`linear`**: Scales linearly with the kept ratio.
+    $$ \mathcal{L}_{\text{final}} = \mathcal{L}_{\text{ppo}} \times \text{ratio} $$
+    -   *Intuition*: If we only keep 10% of the data, we scale the update down by 10% to prevent over-fitting to this small subset.
+
+3.  **`sqrt`**: Scales by the square root of the kept ratio.
+    $$ \mathcal{L}_{\text{final}} = \mathcal{L}_{\text{ppo}} \times \sqrt{\text{ratio}} $$
+    -   *Intuition*: A milder dampening than linear.
+
+### Implementation Details
+-   **Trainer**: The kept ratio is calculated in `ragen/trainer/agent_trainer.py`.
+-   **Loss Scaling**: The scaling is applied directly to the **advantages** in `ragen/trainer/agent_trainer.py` (after `compute_advantage`).
+    ```python
+    if filter_loss_scaling == "linear":
+        batch.batch["advantages"] *= filter_kept_ratio
+    ```
+    This effectively scales the policy gradient updates.
+
+---
+
+## 3. Reward Variance Early Stopping
+To prevent training on collapsed or uninformative rollout groups, we implemented an early stopping mechanism based on reward variance.
+
+### Concept
+The trainer monitors the reward standard deviation (`rollout/in_group_reward_std`) at the successful training-step level.
+
+1.  **Baseline Generation**: During the first 10 successful training steps, the trainer calculates the average reward variance ($V_{base}$).
+2.  **Monitoring**: A sliding window of the last 10 successful training steps is maintained (starts after baseline is ready).
+3.  **Stopping Condition**: If all 10 consecutive step variances are less than 10% of $V_{base}$, training is stopped.
+    $$ \forall i \in \{1 \dots 10\}: V_i < 0.1 \times V_{base} \implies \text{Stop Training} $$
+
+### Implementation
+-   **Baseline**: Average of `rollout/in_group_reward_std` for `global_steps` 1-10.
+-   **Sliding Window**: Uses a `collections.deque(maxlen=10)` to track the most recent successful training steps.
+-   **Metric**: Logs `early_stopped/reward_variance_collapse: 1.0` when triggered.
+
+### 2. Success-Based Early Stopping
+To prevent wasting compute on environments where the model is failing to learn, we implemented an early stopping mechanism based on validation success rates.
+
+- **Condition**: If the success rate for a specific environment (e.g., `val-env/CoordSokoban/success`) remains below **1% (0.01)** for **5 consecutive** validation steps, the training is stopped.
+- **Metric**: Logs `early_stopped/low_validation_success: 1.0` when triggered.
+
+---
+
+A unified script `scripts/runs/run_filtering_final.sh` is provided to run the validated set of filtering experiments.
+
+### Usage
+```bash
+# Run experiments across available GPUs (e.g., 2 GPUs per experiment)
+bash scripts/runs/run_filtering_final.sh 2
+```
+
+### Features
+-   **PPO Focused**: All experiments in this suite use the PPO algorithm.
+-   **400 Steps**: Standardized training length.
+-   **Auto-Scheduling**: Automatically detects available GPUs and distributes experiments.
+-   **Metric Coverage**: Covers `reward_variance`, `entropy`, `entropy_variance`, and `length`.
+-   **Automatic Skip**: Tracks progress in `filter_final_donelist.txt` to avoid redundant runs.
+
+---
+
+## 5. Code References
+-   **Filtering Logic**: `ragen/trainer/rollout_filter.py`
+-   **Trainer Integration**: `ragen/trainer/agent_trainer.py`
+-   **Early Stopping Logic**: `RayAgentTrainer` in `ragen/trainer/agent_trainer.py`
+-   **Loss Scaling Implementation**: `verl/verl/workers/actor/dp_actor.py` (specifically `DataParallelPPOActor.update_policy`)
+-   **Configuration**: `config/base.yaml` and `verl/verl/workers/config/actor.py`
+
+---
+
+## 6. Troubleshooting
+
+### `AssertionError: old_log_probs` Collision
+If you use `rollout_filter_metric=entropy`, you might encounter an `AssertionError` during the `batch.union` operation in `agent_trainer.py`.
+
+-   **Cause**: The `EntropyRolloutFilter` recomputes log probabilities to calculate entropy and returns them in the `DataProto`. The trainer also recomputes log probabilities for the PPO update. `DataProto.union` rejects keys that already exist if they are not the exact same tensor instance.
+-   **Resolution**: The filter has been updated to only include the `entropys` key and prune the redundant `old_log_probs` before unioning with the main batch.

gradient_analysis/plot_icml_steps.py

@@ -0,0 +1,199 @@
+import os
+import json
+import argparse
+from typing import Dict, List, Tuple
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def _load_metrics(path: str) -> Dict:
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def _bucket_sort_key(bucket: str) -> Tuple[int, int, str]:
+    if bucket.startswith("bucket_"):
+        suffix = bucket.split("_", 1)[1]
+        if suffix.isdigit():
+            return (0, int(suffix), bucket)
+    return (1, 0, bucket)
+
+
+def _extract_buckets(metrics: Dict) -> List[str]:
+    buckets = set()
+    for k in metrics.keys():
+        if k.startswith("grad_norm/bucket_"):
+            parts = k.split("/")
+            if len(parts) >= 2:
+                buckets.add(parts[1])
+    return sorted(buckets, key=_bucket_sort_key)
+
+
+def _rv_stats(metrics: Dict, buckets: List[str]) -> Tuple[List[float], List[float], List[float]]:
+    means, mins, maxs = [], [], []
+    for b in buckets:
+        means.append(float(metrics.get(f"grad_norm/{b}/reward_std_mean", 0.0)))
+        mins.append(float(metrics.get(f"grad_norm/{b}/reward_std_min", 0.0)))
+        maxs.append(float(metrics.get(f"grad_norm/{b}/reward_std_max", 0.0)))
+    return means, mins, maxs
+
+
+def _grad_series(metrics: Dict, buckets: List[str]) -> Tuple[List[float], List[float], List[float]]:
+    task = []
+    kl = []
+    ent = []
+    for b in buckets:
+        task.append(float(metrics.get(f"grad_norm/{b}/task", 0.0)))
+        kl.append(float(metrics.get(f"grad_norm/{b}/kl", 0.0)))
+        ent.append(float(metrics.get(f"grad_norm/{b}/entropy", 0.0)))
+    return task, kl, ent
+
+
+def _default_step_dir(mode: str, step: str) -> str:
+    base_dir = os.path.dirname(__file__)
+    return os.path.join(base_dir, "data", mode, step)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="ICML paper plots: step 0/20/40 grid.")
+    parser.add_argument("--mode", choices=["grpo", "ppo"], default="grpo", help="Which dataset to plot")
+    parser.add_argument("--step0-dir", default=None, help="Directory with metrics json for step 0")
+    parser.add_argument("--step20-dir", default=None, help="Directory with metrics json for step 20")
+    parser.add_argument("--step40-dir", default=None, help="Directory with metrics json for step 40")
+    parser.add_argument("--out", default="icml_step0_20_40_grid.png", help="Output PNG path")
+    args = parser.parse_args()
+
+    step0_dir = args.step0_dir or _default_step_dir(args.mode, "step0")
+    step20_dir = args.step20_dir or _default_step_dir(args.mode, "step20")
+    step40_dir = args.step40_dir or _default_step_dir(args.mode, "step40")
+
+    metrics0 = _load_metrics(os.path.join(step0_dir, "metrics.json"))
+    metrics20 = _load_metrics(os.path.join(step20_dir, "metrics.json"))
+    metrics40 = _load_metrics(os.path.join(step40_dir, "metrics.json"))
+
+    buckets = _extract_buckets(metrics20)
+    buckets = [b for b in buckets if b in _extract_buckets(metrics40)]
+    buckets = [b for b in buckets if b in _extract_buckets(metrics0)]
+    labels = [b.replace("_", " ") for b in buckets]
+
+    rv20_means, rv20_mins, rv20_maxs = _rv_stats(metrics20, buckets)
+    rv40_means, rv40_mins, rv40_maxs = _rv_stats(metrics40, buckets)
+    task20, kl20, ent20 = _grad_series(metrics20, buckets)
+    task40, kl40, ent40 = _grad_series(metrics40, buckets)
+    reg20 = [k + e for k, e in zip(kl20, ent20)]
+    reg40 = [k + e for k, e in zip(kl40, ent40)]
+    rv0_means, rv0_mins, rv0_maxs = _rv_stats(metrics0, buckets)
+    task0, kl0, ent0 = _grad_series(metrics0, buckets)
+    reg0 = [k + e for k, e in zip(kl0, ent0)]
+
+    fig, axes = plt.subplots(3, 3, figsize=(16, 12), sharex="col")
+    color_rv = "#1f78b4"
+    color_task = "#e67e22"
+    color_reg = "#16a085"
+
+    positions = np.arange(len(buckets))
+    box_width = 0.35
+    def _draw_interval_mean(ax, x, vmin, vmax, vmean, color):
+        if vmax < vmin:
+            vmin, vmax = vmax, vmin
+        yerr = [[max(0.0, vmean - vmin)], [max(0.0, vmax - vmean)]]
+        ax.errorbar(
+            [x],
+            [vmean],
+            yerr=yerr,
+            fmt="o",
+            color=color,
+            markersize=5,
+            capsize=4,
+            linewidth=1.2,
+        )
+
+    # rows: step0 (if provided), step20, step40
+    steps = [
+        ("Step 0", rv0_means, rv0_mins, rv0_maxs, task0, reg0),
+        ("Step 20", rv20_means, rv20_mins, rv20_maxs, task20, reg20),
+        ("Step 40", rv40_means, rv40_mins, rv40_maxs, task40, reg40),
+    ]
+
+    col_titles = [
+        "Reward Variance by bucket",
+        "Task gradient norm vs Reward Variance",
+        "Regularizer gradient norm (KL+Entropy) vs RV",
+    ]
+    col_captions = [
+        "RV quantile buckets. (Q1 -> Q6)",
+        "Bucket RV (log scale).",
+        "Bucket RV (log scale).",
+    ]
+
+    for r, (step_name, rv_means, rv_mins, rv_maxs, task, reg) in enumerate(steps):
+        # (a) RV per bucket interval + mean
+        ax = axes[r][0]
+        for i, x in enumerate(positions):
+            _draw_interval_mean(
+                ax,
+                x,
+                rv_mins[i],
+                rv_maxs[i],
+                rv_means[i],
+                color=color_rv,
+            )
+        ax.set_yscale("log")
+        ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
+        ax.set_ylabel(f"{step_name}\nReward Variance (Std)")
+        if r == 0:
+            ax.set_title("(a) " + col_titles[0], fontweight="bold")
+        ax.set_xticks(positions)
+        ax.set_xticklabels(labels if r == len(steps) - 1 else [])
+
+        # (b) Task vs RV
+        ax = axes[r][1]
+        ax.plot(rv_means, task, linestyle="-", marker="o", color=color_task, markersize=5)
+        ax.set_xscale("log")
+        ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
+        ax.set_ylabel(f"{step_name}\nTask grad norm")
+        if r == 0:
+            ax.set_title("(b) " + col_titles[1], fontweight="bold")
+        # if r == len(steps) - 1:
+        #     ax.set_xlabel("RV mean")
+
+        # (c) Reg vs RV
+        ax = axes[r][2]
+        ax.plot(rv_means, reg, linestyle="-", marker="o", color=color_reg, markersize=5)
+        ax.set_xscale("log")
+        ax.set_ylim(0.0, 0.1)
+        ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
+        ax.set_ylabel(f"{step_name}\nKL+Entropy grad norm")
+        if r == 0:
+            ax.set_title("(c) " + col_titles[2], fontweight="bold")
+        # if r == len(steps) - 1:
+        #     ax.set_xlabel("RV mean")
+
+    # style spines
+    for row in axes:
+        for a in row:
+            a.spines["top"].set_visible(False)
+            a.spines["right"].set_visible(False)
+
+    # captions under each column
+    for c, caption in enumerate(col_captions):
+        ax = axes[-1][c]
+        ax.text(
+            0.5,
+            -0.15,
+            caption,
+            transform=ax.transAxes,
+            ha="center",
+            va="top",
+            fontsize=10,
+            fontweight="bold",
+        )
+
+    plt.tight_layout()
+    plt.savefig(args.out, dpi=300)
+    print(f"Saved figure to {os.path.abspath(args.out)}")
+
+
+if __name__ == "__main__":
+    main()

outputs/2026-04-30/13-11-58/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-04-30/13-11-58
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-11/10-46-20/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.8
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 8192
+    max_model_len: 3600
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 600
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: 100
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 5
+  action_sep: '||'
+  max_actions_per_turn: 2
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - CoordSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - CoordSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-11/10-46-20/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-11/10-46-20
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-11/11-35-35/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-11/11-35-35
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-11/11-35-35/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft

outputs/2026-05-11/11-38-43/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.8
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 8192
+    max_model_len: 3600
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 5
+  action_sep: '||'
+  max_actions_per_turn: 2
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-11/14-33-26/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.8
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 8192
+    max_model_len: 3600
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 1.0
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen_rlvtr_3b
+  experiment_name: sokoban1_3b_rft_second
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 5
+  action_sep: '||'
+  max_actions_per_turn: 2
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-11/17-12-08/.hydra/overrides.yaml

@@ -0,0 +1,6 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/wanghy/LLaMA-Factory/saves/qwen3/full/sft/qwen2.5_3B_it_sokoban_box1_rft
+- trainer.save_freq=1000
+- trainer.default_local_dir=/mnt/general/wanghy/RAGEN_v2/saves/qwen3b_it_sokoban1_rft_rl

outputs/2026-05-12/09-55-39/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.8
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-12/09-55-39/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-12/09-55-39
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-12/09-55-39/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct

outputs/2026-05-12/15-16-37/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-13/10-39-50/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-13/10-39-50/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-13/10-39-50
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-15/14-05-31/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sudoku-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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 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: easy
+      render_format: with_feedback
+      show_conflicts: true
+      show_valid_numbers: true
+      max_steps: 81
+  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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 8
+  val:
+    env_groups: 32
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 32
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-15/14-05-31/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _8_sudoku
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-15/14-05-31
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-15/14-05-31/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct

outputs/2026-05-15/14-11-35/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sudoku-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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: <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: true
+      show_valid_numbers: true
+      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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 8
+  val:
+    env_groups: 32
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 32
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-15/14-11-35/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _8_sudoku
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-15/14-11-35
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-15/14-11-35/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct

outputs/2026-05-15/14-56-09/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 400
+  project_name: ragen
+  experiment_name: sudoku-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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: <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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 8
+  val:
+    env_groups: 32
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 32
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-15/14-56-09/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=400
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=400
+    id: ???
+    num: ???
+    config_name: _8_sudoku
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-15/14-56-09
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-15/14-56-09/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=400
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct

outputs/2026-05-15/15-01-19/.hydra/config.yaml

@@ -0,0 +1,989 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 400
+  project_name: ragen
+  experiment_name: sudoku-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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: <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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 8
+  val:
+    env_groups: 32
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSudoku
+      n_groups:
+      - 32
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-15/15-01-19/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=400
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct,trainer.total_training_steps=400
+    id: ???
+    num: ???
+    config_name: _8_sudoku
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-15/15-01-19
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-15/15-01-19/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=400
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct

outputs/2026-05-22/21-57-02/.hydra/config.yaml

@@ -0,0 +1,1003 @@
+actor_rollout_ref:
+  actor:
+    optim:
+      _target_: verl.workers.config.FSDPOptimizerConfig
+      lr: 1.0e-06
+      lr_warmup_steps_ratio: 0.0
+      total_training_steps: -1
+      weight_decay: 0.01
+      lr_warmup_steps: -1
+      betas:
+      - 0.9
+      - 0.999
+      clip_grad: 1.0
+      min_lr_ratio: 0.0
+      num_cycles: 0.5
+      warmup_style: constant
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    _target_: verl.workers.config.FSDPActorConfig
+    strategy: fsdp
+    ppo_mini_batch_size: ${ppo_mini_batch_size}
+    ppo_micro_batch_size: null
+    ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+    use_dynamic_bsz: false
+    ppo_max_token_len_per_gpu: 16384
+    clip_ratio: 0.2
+    clip_ratio_low: 0.2
+    clip_ratio_high: 0.28
+    freeze_vision_tower: false
+    policy_loss:
+      _target_: verl.workers.config.PolicyLossConfig
+      loss_mode: vanilla
+      clip_cov_ratio: 0.0002
+      clip_cov_lb: 1.0
+      clip_cov_ub: 5.0
+      kl_cov_ratio: 0.0002
+      ppo_kl_coef: 0.1
+    clip_ratio_c: 3.0
+    loss_agg_mode: token-mean
+    entropy_coeff: 0.001
+    tis_imp_ratio_cap: -1
+    use_kl_loss: false
+    use_torch_compile: true
+    kl_loss_coef: 0.0
+    kl_loss_type: kl
+    ppo_epochs: 1
+    shuffle: false
+    checkpoint:
+      _target_: verl.trainer.config.CheckpointConfig
+      save_contents:
+      - model
+      - optimizer
+      - extra
+      load_contents: ${.save_contents}
+      async_save: false
+    use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    grad_clip: 1.0
+    ulysses_sequence_parallel_size: 1
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+    use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false}
+    use_ref: true
+    grpo_advantage_length_weight: ${grpo_advantage_length_weight}
+    filter_loss_scaling: none
+  ref:
+    strategy: ${actor_rollout_ref.actor.strategy}
+    use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true}
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: false
+      all_ranks: false
+      ranks: []
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config:
+        nsys:
+          _target_: verl.utils.profiler.config.NsightToolConfig
+          discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+        npu:
+          _target_: verl.utils.profiler.config.NPUToolConfig
+          contents: []
+          level: level1
+          analysis: true
+          discrete: false
+        torch:
+          _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+          step_start: 0
+          step_end: null
+        torch_memory:
+          _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+          trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+          stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    model: null
+    ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1}
+    entropy_from_logits_with_chunking: false
+    entropy_checkpointing: false
+  rollout:
+    _target_: verl.workers.config.RolloutConfig
+    name: vllm
+    mode: sync
+    temperature: 1
+    top_k: -1
+    top_p: 1
+    prompt_length: 1
+    response_length: 400
+    dtype: bfloat16
+    gpu_memory_utilization: 0.7
+    ignore_eos: false
+    enforce_eager: true
+    cudagraph_capture_sizes: null
+    free_cache_engine: true
+    tensor_model_parallel_size: 1
+    data_parallel_size: 1
+    expert_parallel_size: 1
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    max_num_seqs: 1024
+    enable_chunked_prefill: true
+    enable_prefix_caching: true
+    load_format: auto
+    log_prob_micro_batch_size: null
+    log_prob_micro_batch_size_per_gpu: ${log_prob_micro_batch_size_per_gpu}
+    log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+    log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384}
+    disable_log_stats: true
+    do_sample: true
+    'n': 1
+    over_sample_rate: 0
+    multi_stage_wake_up: false
+    engine_kwargs:
+      vllm: {}
+      sglang: {}
+    val_kwargs:
+      _target_: verl.workers.config.SamplingConfig
+      top_k: -1
+      top_p: 1.0
+      temperature: 0.5
+      'n': 1
+      do_sample: true
+    multi_turn:
+      _target_: verl.workers.config.MultiTurnConfig
+      enable: false
+      max_assistant_turns: null
+      tool_config_path: null
+      max_user_turns: null
+      max_parallel_calls: 1
+      max_tool_response_length: 256
+      tool_response_truncate_side: middle
+      interaction_config_path: null
+      use_inference_chat_template: false
+      tokenization_sanity_check_mode: strict
+      format: hermes
+      num_repeat_rollouts: null
+    calculate_log_probs: false
+    agent:
+      _target_: verl.workers.config.AgentLoopConfig
+      num_workers: 8
+      agent_loop_config_path: null
+      custom_async_server:
+        _target_: verl.workers.config.CustomAsyncServerConfig
+        path: null
+        name: null
+    update_weights_bucket_megabytes: 512
+    trace:
+      _target_: verl.workers.config.TraceConfig
+      backend: null
+      token2text: false
+    skip_rollout: false
+    skip_dump_dir: /tmp/rollout_dump
+    skip_tokenizer_init: true
+    profiler:
+      _target_: verl.utils.profiler.ProfilerConfig
+      tool: ${oc.select:global_profiler.tool,null}
+      enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false}
+      all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false}
+      ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]}
+      save_path: ${oc.select:global_profiler.save_path,null}
+      tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+    layered_summon: false
+    rollout_filter_value: 0.9
+    rollout_filter_strategy: top_p
+    rollout_filter_type: largest
+    rollout_filter_include_zero: true
+    rollout_filter_top_p_prob_mode: linear
+    rollout_filter_selection_eps: 0.01
+    rollout_filter_empty_stop_steps: 5
+    rollout_filter_metric: reward_variance
+    gradient_analysis_num_buckets: 6
+    gradient_analysis_bucket_mode: quantile
+  model:
+    _target_: verl.workers.config.HFModelConfig
+    path: ${model_path}
+    hf_config_path: null
+    tokenizer_path: null
+    use_shm: false
+    trust_remote_code: false
+    custom_chat_template: null
+    external_lib: null
+    override_config: {}
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+    exclude_modules: null
+    use_liger: false
+    use_fused_kernels: false
+    fused_kernel_options:
+      impl_backend: torch
+  hybrid_engine: true
+  nccl_timeout: 600
+data:
+  tokenizer: null
+  use_shm: false
+  train_files: ~/data/rlhf/gsm8k/train.parquet
+  val_files: ~/data/rlhf/gsm8k/test.parquet
+  prompt_key: prompt
+  reward_fn_key: data_source
+  max_prompt_length: null
+  max_response_length: null
+  train_batch_size: null
+  val_batch_size: null
+  return_raw_input_ids: false
+  return_raw_chat: false
+  return_full_prompt: false
+  shuffle: true
+  dataloader_num_workers: 8
+  validation_shuffle: false
+  filter_overlong_prompts: false
+  filter_overlong_prompts_workers: 1
+  truncation: error
+  image_key: images
+  video_key: videos
+  trust_remote_code: false
+  custom_cls:
+    path: null
+    name: null
+  return_multi_modal_inputs: true
+  sampler:
+    class_path: null
+    class_name: null
+  datagen:
+    path: null
+    name: null
+  apply_chat_template_kwargs: {}
+critic:
+  optim:
+    _target_: verl.workers.config.FSDPOptimizerConfig
+    lr: 1.0e-05
+    lr_warmup_steps_ratio: 0.0
+    total_training_steps: -1
+    weight_decay: 0.01
+    lr_warmup_steps: -1
+    betas:
+    - 0.9
+    - 0.999
+    clip_grad: 1.0
+    min_lr_ratio: 0.0
+    num_cycles: 0.5
+    warmup_style: constant
+  model:
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      optimizer_offload: false
+      offload_policy: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+      model_dtype: fp32
+      use_orig_params: false
+      ulysses_sequence_parallel_size: 1
+      entropy_from_logits_with_chunking: false
+      use_torch_compile: true
+      entropy_checkpointing: false
+      forward_only: false
+      strategy: fsdp
+    path: ${model_path}
+    tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"}
+    override_config: {}
+    external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null}
+    trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false}
+    _target_: verl.workers.config.FSDPCriticModelCfg
+    use_shm: false
+    enable_gradient_checkpointing: true
+    enable_activation_offload: false
+    use_remove_padding: false
+    lora_rank: ${lora.rank}
+    lora_alpha: ${lora.alpha}
+    target_modules: ${lora.target_modules}
+  _target_: verl.workers.config.FSDPCriticConfig
+  rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1}
+  strategy: fsdp
+  enable: null
+  ppo_mini_batch_size: ${ppo_mini_batch_size}
+  ppo_micro_batch_size: null
+  ppo_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+  use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false}
+  ppo_max_token_len_per_gpu: 32768
+  forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu}
+  ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1}
+  shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false}
+  cliprange_value: 0.5
+  loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean}
+  checkpoint:
+    _target_: verl.trainer.config.CheckpointConfig
+    save_contents:
+    - model
+    - optimizer
+    - extra
+    load_contents: ${.save_contents}
+    async_save: false
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config:
+      nsys:
+        _target_: verl.utils.profiler.config.NsightToolConfig
+        discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete}
+      npu:
+        _target_: verl.utils.profiler.config.NPUToolConfig
+        contents: []
+        level: level1
+        analysis: true
+        discrete: false
+      torch:
+        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+        step_start: 0
+        step_end: null
+      torch_memory:
+        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+        trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000}
+        stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32}
+  forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null}
+  forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null}
+  ulysses_sequence_parallel_size: 1
+  grad_clip: 1.0
+reward_model:
+  enable: false
+  enable_resource_pool: false
+  n_gpus_per_node: 0
+  nnodes: 0
+  strategy: fsdp
+  model:
+    input_tokenizer: ${actor_rollout_ref.model.path}
+    path: ~/models/FsfairX-LLaMA3-RM-v0.1
+    external_lib: ${actor_rollout_ref.model.external_lib}
+    trust_remote_code: false
+    use_shm: false
+    use_remove_padding: false
+    use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels}
+    fsdp_config:
+      _target_: verl.workers.config.FSDPEngineConfig
+      wrap_policy:
+        min_num_params: 0
+      param_offload: false
+      reshard_after_forward: true
+      fsdp_size: -1
+      forward_prefetch: false
+  micro_batch_size: null
+  micro_batch_size_per_gpu: null
+  max_length: null
+  use_dynamic_bsz: ${critic.use_dynamic_bsz}
+  forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu}
+  reward_manager: naive
+  launch_reward_fn_async: false
+  sandbox_fusion:
+    url: null
+    max_concurrent: 64
+    memory_limit_mb: 1024
+  profiler:
+    _target_: verl.utils.profiler.ProfilerConfig
+    tool: ${oc.select:global_profiler.tool,null}
+    enable: false
+    all_ranks: false
+    ranks: []
+    save_path: ${oc.select:global_profiler.save_path,null}
+    tool_config: ${oc.select:actor_rollout_ref.actor.profiler.tool_config,null}
+  ulysses_sequence_parallel_size: 1
+custom_reward_function:
+  path: null
+  name: compute_score
+algorithm:
+  _target_: verl.trainer.config.AlgoConfig
+  gamma: 1.0
+  lam: 1.0
+  adv_estimator: gae
+  norm_adv_by_std_in_grpo: true
+  use_kl_in_reward: false
+  kl_penalty: kl
+  kl_ctrl:
+    _target_: verl.trainer.config.KLControlConfig
+    type: fixed
+    kl_coef: 0.0
+    horizon: 10000
+    target_kl: 0.1
+  use_pf_ppo: false
+  pf_ppo:
+    reweight_method: pow
+    weight_pow: 2.0
+  high_level_gamma: 0.95
+  bi_level_gae: false
+  zero_task_advantage: false
+  soft_advantage_reweight: false
+trainer:
+  balance_batch: true
+  total_epochs: 30
+  total_training_steps: 1000
+  project_name: ragen
+  experiment_name: sokoban-main
+  logger:
+  - console
+  - wandb
+  log_val_generations: 0
+  rollout_data_dir: null
+  validation_data_dir: null
+  nnodes: 1
+  n_gpus_per_node: 8
+  save_freq: -1
+  esi_redundant_time: 0
+  resume_mode: auto
+  resume_from_path: null
+  val_before_train: true
+  val_only: false
+  test_freq: 10
+  critic_warmup: 0
+  default_hdfs_dir: null
+  del_local_ckpt_after_load: false
+  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
+  max_actor_ckpt_to_keep: 1
+  max_critic_ckpt_to_keep: 1
+  ray_wait_register_center_timeout: 300
+  device: cuda
+  use_legacy_worker_impl: auto
+  local_log_dir: results/
+  validation_steps: 1
+  generations_to_log_to_wandb:
+    val: 20
+  log_group_rv_table: false
+  gradient_analysis_mode: false
+  gradient_analysis_every: 50
+  gradient_analysis_env_groups: null
+  gradient_analysis_group_size: null
+  gradient_analysis_log_prefilter: false
+  gradient_analysis_only: false
+  exit_after_gradient_analysis: false
+global_profiler:
+  _target_: verl.utils.profiler.ProfilerConfig
+  tool: null
+  steps: null
+  profile_continuous_steps: false
+  save_path: outputs/profile
+  global_tool_config:
+    nsys:
+      _target_: verl.utils.profiler.config.NsightToolConfig
+      discrete: false
+      controller_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+      worker_nsight_options:
+        trace: cuda,nvtx,cublas,ucx
+        cuda-memory-usage: 'true'
+        cuda-graph-trace: graph
+        capture-range: cudaProfilerApi
+        capture-range-end: null
+        kill: none
+    torch_memory:
+      trace_alloc_max_entries: 100000
+      stack_depth: 32
+      context: all
+      stacks: all
+      kw_args: {}
+ray_kwargs:
+  ray_init:
+    num_cpus: null
+  timeline_json_file: null
+custom_envs:
+  SimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are a careful Sokoban solver. Use these reusable abstractions\
+      \ when reasoning:\n1. First compare the box position with the target position;\
+      \ the useful push directions are usually the directions that reduce their row/column\
+      \ distance.\n2. Before pushing, move the player to the square opposite the intended\
+      \ push direction. A move that only repositions the player can be useful if it\
+      \ sets up the next push.\n3. Never push a box into a wall, corner, or narrow\
+      \ dead end unless that square is the target or clearly on the only path to the\
+      \ target.\n4. Prefer short plans that move the single box steadily toward the\
+      \ target; avoid wandering moves that do not improve player position or box position.\n\
+      5. If the box and target are aligned in the same row or column, try to keep\
+      \ the box on that line and push along it.\n6. If they are not aligned, first\
+      \ push to fix one coordinate, then reposition and push to fix the other coordinate.\n\
+      7. Check that after each push, the player can still reach the next required\
+      \ pushing side of the box.\n8. Output only the required XML-like format and\
+      \ choose valid Sokoban actions.\n\nYou are the player and you need to push all\
+      \ boxes to targets. \nWhen you are right next to a box, you can push it by moving\
+      \ in the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box. \nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+  LargerSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 8
+      dim_y: 8
+      num_boxes: 2
+      max_steps: 100
+      search_depth: 10
+  SokobanDifferentGridVocab:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and\
+      \ you need to push all boxes to targets. \nWhen you are right next to a box,\
+      \ you can push it by moving in the same direction. \nYou cannot push a box through\
+      \ a wall, and you cannot pull a box. \nThe answer should be a sequence of actions,\
+      \ like <answer>Right || Right || Up</answer>\n"
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      search_depth: 30
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      grid_lookup:
+        0: W
+        1: .
+        2: G
+        3: C
+        4: B
+        5: A
+        6: '@'
+      grid_vocab:
+        W: wall
+        .: empty
+        G: target
+        C: box on target
+        B: box
+        A: player
+        '@': player on target
+  CoordSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: "You are solving the Sokoban puzzle. You are the player and you\
+      \ need to push all boxes to targets.\nYou are provided with a symbol grid and\
+      \ the zero-indexed coordinates of the player, each box, and each target. \n\
+      Coordinates range from the top-left corner (0, 0) to the bottom-right corner\
+      \ (5, 5). \nWhen you are exactly next to a box, you can push it by moving in\
+      \ the same direction. \nYou cannot push a box through a wall, and you cannot\
+      \ pull a box.\nThe answer should be a sequence of actions, like <answer>Right\
+      \ || Right || Up</answer>.\n"
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      observation_format: grid_coord
+  VisualSimpleSokoban:
+    env_type: sokoban
+    max_actions_per_traj: 10
+    env_instruction: You are solving the Sokoban puzzle. You are the player and you
+      need to push all boxes to targets. When you are right next to a box, you can
+      push it by moving in the same direction. You cannot push a box through a wall,
+      and you cannot pull a box. The answer should be a sequence of actions, like
+      <answer>Right || Right || Up</answer>
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      dim_x: 6
+      dim_y: 6
+      num_boxes: 1
+      max_steps: 100
+      render_mode: rgb_array
+  Alfworld:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_in_distribution
+  AlfworldOOD:
+    env_type: alfworld
+    max_actions_per_traj: 50
+    parallel_friendly: false
+    max_workers: 1
+    env_instruction: 'You are an expert agent in the ALFRED Embodied Environment.
+
+      Complete household tasks by navigating and interacting with objects.
+
+
+      You should first reason step-by-step about the current situation. This reasoning
+      process MUST be enclosed within <think> </think> tags.
+
+      Once you''ve finished your reasoning, you should choose an admissible action
+      for current step and present it within <answer>...</answer> tags.
+
+      '
+    max_tokens: 512
+    env_config:
+      eval_dataset: eval_out_of_distribution
+  Countdown:
+    env_type: countdown
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving the Countdown puzzle. You should use the num
+      list to create an equation that equals the target. Example answer format: <think>
+      To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3
+      = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config: null
+  Bandit:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: train
+  BanditTest:
+    env_type: bandit
+    max_actions_per_traj: 1
+    env_instruction: ''
+    max_tokens: 100
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      split: test
+  DeepCoder:
+    env_type: deepcoder
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving a coding task. Provide a complete Python function
+      solution only. Format: <answer>...</answer>'
+    max_tokens: 8000
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 1
+  FrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      success_rate: 0.8
+  CoordFrozenLake:
+    env_type: frozen_lake
+    max_actions_per_traj: 10
+    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).
+
+      Beware that the ice is slippery, so the agent might slide and end up in an unintended
+      tile.
+
+      Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+
+      '
+    max_tokens: 120
+    parallel_friendly: false
+    max_workers: 32
+    env_config:
+      observation_format: grid_coord
+      success_rate: 0.8
+  MetamathQA:
+    env_type: metamathqa
+    max_actions_per_traj: 1
+    env_instruction: 'You are solving Math problems. '
+    max_tokens: 100
+    env_config: null
+  WebShopFull:
+    env_type: webshop
+    max_actions_per_traj: 15
+    env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce
+      environment.
+    max_tokens: 200
+    env_config:
+      dataset: full
+  WebShop:
+    env_type: webshop
+    max_actions_per_traj: 9
+    env_instruction: 'You are browsing an online shop. Based on the instruction, buy
+      a product that close to the production description. You need to search, read
+      the search results, pick a product, choose the size and color and buy. You should
+      only choose action from the available actions list provided later.  Example
+      process: I need a gingko light and 20x20 pillow cover that is hand painted.
+      First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket
+      with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+    max_tokens: 200
+    env_config:
+      dataset: small
+  Lean:
+    env_type: lean
+    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
+    parallel_friendly: true
+    max_workers: 32
+    env_config: null
+  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: <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
+  SearchQA:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+  SearchQAMock:
+    env_type: search
+    max_actions_per_traj: 10
+    env_instruction: "You are a search agent answering questions by searching for\
+      \ information.\nUse search[your query] to find relevant documents, and finish[your\
+      \ answer] to submit your final answer.\n\nYou should first reason step-by-step\
+      \ about the current situation. This reasoning process MUST be enclosed within\
+      \ <think> </think> tags.\nThen provide your action within <answer>...</answer>\
+      \ tags.\n\nExamples:\n  <think>I need to find information about Ben Platt's\
+      \ father.</think><answer>search[Ben Platt father parent]</answer>\n  <think>Based\
+      \ on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry\
+      \ Platt]</answer>\n"
+    max_tokens: 300
+    parallel_friendly: true
+    max_workers: 32
+    env_config:
+      max_steps: 10
+      max_search_results: 5
+      mock_mode: true
+  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
+  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
+system:
+  CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+seed:
+  train: 10000
+  val: 123
+micro_batch_size_per_gpu: 1
+log_prob_micro_batch_size_per_gpu: ${micro_batch_size_per_gpu}
+ppo_mini_batch_size: 32
+model_path: /mnt/general/share/model/Qwen/Qwen2.5-7B-Instruct
+enable_response_mask: true
+grpo_advantage_length_weight: false
+lora:
+  rank: 0
+  alpha: 64
+  target_modules: all-linear
+agent_proxy:
+  context_window_mode: full
+  max_context_window: -1
+  batch_adjust_mode: copy
+  max_turn: 15
+  action_sep: '||'
+  max_actions_per_turn: 1
+  use_turn_scores: false
+  enable_think: true
+  reward_normalization:
+    grouping: state
+    method: identity
+collapse_detection:
+  compute_freq: 5
+  micro_batch_size: 128
+  first_turn_enabled: true
+  multi_turn_enabled: true
+  num_samples: 64
+es_manager:
+  format_penalty: -0.1
+  train:
+    env_groups: 8
+    group_size: 16
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 8
+  val:
+    env_groups: 512
+    group_size: 1
+    env_configs:
+      tags:
+      - SimpleSokoban
+      n_groups:
+      - 512
+ctx_manager:
+  generation:
+    gen_config:
+      response_length: ${actor_rollout_ref.rollout.response_length}
+      temperature: ${actor_rollout_ref.rollout.temperature}
+      top_p: ${actor_rollout_ref.rollout.top_p}
+      top_k: ${actor_rollout_ref.rollout.top_k}
+      kwargs: null

outputs/2026-05-22/21-57-02/.hydra/hydra.yaml

@@ -0,0 +1,177 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath:
+  - pkg://verl.trainer.config
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+    - actor_rollout_ref.rollout.rollout_filter_value=0.9
+    - trainer.total_training_steps=1000
+    - model_path=/mnt/general/share/model/Qwen/Qwen2.5-7B-Instruct
+  job:
+    name: train
+    chdir: null
+    override_dirname: actor_rollout_ref.rollout.rollout_filter_strategy=top_p,actor_rollout_ref.rollout.rollout_filter_value=0.9,model_path=/mnt/general/share/model/Qwen/Qwen2.5-7B-Instruct,trainer.total_training_steps=1000
+    id: ???
+    num: ???
+    config_name: _2_sokoban
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /mnt/general/wanghy/RAGEN_v2
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /mnt/general/wanghy/RAGEN_v2/config
+      schema: file
+      provider: main
+    - path: /mnt/general/wanghy/RAGEN_v2/verl/verl/trainer/config
+      schema: file
+      provider: command-line
+    - path: verl.trainer.config
+      schema: pkg
+      provider: hydra.searchpath in main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /mnt/general/wanghy/RAGEN_v2/outputs/2026-05-22/21-57-02
+    choices:
+      reward_model: dp_reward_model
+      critic: dp_critic
+      critic/../engine@critic.model.fsdp_config: fsdp
+      critic/../optim@critic.optim: fsdp
+      model@actor_rollout_ref.model: hf_model
+      rollout@actor_rollout_ref.rollout: rollout
+      ref@actor_rollout_ref.ref: dp_ref
+      ref/../engine@actor_rollout_ref.ref.fsdp_config: fsdp
+      data: legacy_data
+      actor@actor_rollout_ref.actor: dp_actor
+      actor/../engine@actor_rollout_ref.actor.fsdp_config: fsdp
+      actor/../optim@actor_rollout_ref.actor.optim: fsdp
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

outputs/2026-05-22/21-57-02/.hydra/overrides.yaml

@@ -0,0 +1,4 @@
+- actor_rollout_ref.rollout.rollout_filter_strategy=top_p
+- actor_rollout_ref.rollout.rollout_filter_value=0.9
+- trainer.total_training_steps=1000
+- model_path=/mnt/general/share/model/Qwen/Qwen2.5-7B-Instruct