eval.sh

# 难度测试（三个难度，快速评估）
scripts/eval_selected_envs.sh \
  --model-path Qwen/Qwen2.5-14B-Instruct \
  --gpus 0,1 \
  --override "actor_rollout_ref.rollout.tensor_model_parallel_size=2" \
  frozenlake_difficulty

scripts/eval_selected_envs.sh \
  --model-path Qwen/Qwen2.5-14B-Instruct \
  --gpus 0 \
  --override "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
  --override "es_manager.val.env_configs.tags=[CoordSokoban]" \
  --override "es_manager.val.env_configs.n_groups=[512]" \
  --override "agent_proxy.max_turn=10" \
  --override "actor_rollout_ref.rollout.max_model_len=8192" \
  --override "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
  sokoban

python -m ragen.llm_agent.agent_proxy \
  --config-name _2_sokoban \
  model_path=Qwen/Qwen2.5-14B-Instruct \
  "system.CUDA_VISIBLE_DEVICES=0" \
  "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
  "actor_rollout_ref.rollout.max_model_len=8192" \
  "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
  "seed.val=2042" \
  "es_manager.val.env_groups=2000" \
  "es_manager.val.group_size=5" \
  "es_manager.val.env_configs.tags=[CoordSokoban]" \
  "es_manager.val.env_configs.n_groups=[2000]" \
  "agent_proxy.max_turn=10" \
  "+output.dir=results/sft_data/sokoban_14B_10K_part2"

python -m ragen.llm_agent.agent_proxy \
  --config-name _2_sokoban \
  model_path=Qwen/Qwen2.5-1.5B \
  "system.CUDA_VISIBLE_DEVICES=1" \
  "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
  "actor_rollout_ref.rollout.max_model_len=8192" \
  "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
  "seed.val=2042" \
  "es_manager.val.env_groups=2000" \
  "es_manager.val.group_size=5" \
  "es_manager.val.env_configs.tags=[CoordSokoban]" \
  "es_manager.val.env_configs.n_groups=[2000]" \
  "agent_proxy.max_turn=10" \
  "+output.dir=results/sft_data/sokoban_1.5B_10K_part2"

# 不加载任何模型，秒出结果
python -m ragen.llm_agent.stitch_proxy \
    --config-name _2_sokoban \
    model_path=Qwen/Qwen2.5-14B-Instruct \
    "system.CUDA_VISIBLE_DEVICES=2" \
    "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
    "actor_rollout_ref.rollout.max_model_len=8192" \
    "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
    "seed.val=42" \
    "es_manager.val.env_groups=2000" \
    "es_manager.val.group_size=5" \
    "es_manager.val.env_configs.tags=[CoordSokoban]" \
    "es_manager.val.env_configs.n_groups=[2000]" \
    "agent_proxy.max_turn=10" \
    "+stitch.student_pkl=results/sft_data/sokoban_1.5B_10K/val_rollouts_20260410_220803.pkl" \
    "+stitch.cutoff_turn=2" \
    "+stitch.test=true"

python -m ragen.llm_agent.stitch_proxy \
    --config-name _2_sokoban \
    model_path=Qwen/Qwen2.5-14B-Instruct \
    "system.CUDA_VISIBLE_DEVICES=2" \
    "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
    "actor_rollout_ref.rollout.max_model_len=8192" \
    "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
    "seed.val=42" \
    "es_manager.val.env_groups=2000" \
    "es_manager.val.group_size=5" \
    "es_manager.val.env_configs.tags=[CoordSokoban]" \
    "es_manager.val.env_configs.n_groups=[2000]" \
    "agent_proxy.max_turn=10" \
    "+stitch.student_pkl=results/sft_data/sokoban_1.5B_10K/val_rollouts_20260410_220803.pkl" \
    "+stitch.cutoff_turn=2" \
    "+output.dir=results/sft_data/sokoban_stitch_14B_cut2"

python -m ragen.llm_agent.agent_proxy \
    --config-name _2_sokoban \
    model_path=Qwen/Qwen2.5-1.5B \
    "system.CUDA_VISIBLE_DEVICES=2" \
    "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
    "actor_rollout_ref.rollout.max_model_len=16384" \
    "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
    "seed.val=42" \
    "es_manager.val.env_groups=2000" \
    "es_manager.val.group_size=5" \
    "es_manager.val.env_configs.tags=[CoordSokoban]" \
    "es_manager.val.env_configs.n_groups=[2000]" \
    "agent_proxy.max_turn=20" \
    "+output.dir=results/sft_data/sokoban_hard_1.5B"

# ============================================================
# 生成 10K teacher/student trajectory（MediumFrozenLake，相同地图）
# seed.val 固定相同，保证两个模型跑的是同一批地图
# env_groups=625, group_size=16 → 625×16=10000 episodes
# max_turn=7 → 最多14步，比默认5步更充裕
# ============================================================

# Step 1: Teacher (14B) 生成 trajectory
python -m ragen.llm_agent.agent_proxy \
  --config-name _3_frozen_lake \
  model_path=Qwen/Qwen2.5-14B-Instruct \
  "system.CUDA_VISIBLE_DEVICES=0,1" \
  "actor_rollout_ref.rollout.tensor_model_parallel_size=2" \
  "seed.val=42" \
  "es_manager.val.env_groups=625" \
  "es_manager.val.group_size=16" \
  "es_manager.val.env_configs.tags=[MediumFrozenLake]" \
  "es_manager.val.env_configs.n_groups=[625]" \
  "agent_proxy.max_turn=7" \
  "+output.dir=results/distill_data/teacher_14B_medium"

# Step 2: Student (1.5B) 在相同地图上生成 trajectory
python -m ragen.llm_agent.agent_proxy \
  --config-name _3_frozen_lake \
  model_path=Qwen/Qwen2.5-1.5B \
  "system.CUDA_VISIBLE_DEVICES=0" \
  "seed.val=42" \
  "es_manager.val.env_groups=625" \
  "es_manager.val.group_size=16" \
  "es_manager.val.env_configs.tags=[MediumFrozenLake]" \
  "es_manager.val.env_configs.n_groups=[625]" \
  "agent_proxy.max_turn=7" \
  "+output.dir=results/distill_data/student_1.5B_medium"

# 只看统计
conda run -n colm python3 scripts/inspect_rollout.py \
  --path results/eval_multi/Qwen2.5-1.5B/frozenlake_EasyFrozenLake/val_rollouts_20260410_171534.pkl \
  --summary

# 看前3个 episode（默认）
conda run -n colm python3 scripts/inspect_rollout.py \
  --path results/eval_multi/.../val_rollouts_*.pkl

# 看第5个 episode
conda run -n colm python3 scripts/inspect_rollout.py --path results/eval_multi/Qwen2.5-14B-Instruct/frozenlake_MediumFrozenLake/val_rollouts_20260410_180728.pkl --idx 1 --max_len 9999

# 只看成功的
conda run -n colm python3 scripts/inspect_rollout.py --path ... --success_only

# 只看失败的，显示完整内容（不截断）
conda run -n colm python3 scripts/inspect_rollout.py --path ... --fail_only --max_len 2000
conda run -n colm python3 scripts/dump_rollout.py --path results/eval_multi/Qwen2.5-1.5B/frozenlake_EasyFrozenLake/val_rollouts_20260410_171534.pkl

python -m ragen.llm_agent.agent_proxy \
  --config-name _2_sokoban \
  model_path=Qwen/Qwen2.5-1.5B \
  "system.CUDA_VISIBLE_DEVICES=1" \
  "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
  "actor_rollout_ref.rollout.max_model_len=8192" \
  "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
  "seed.val=2042" \
  "es_manager.val.env_groups=4000" \
  "es_manager.val.group_size=5" \
  "es_manager.val.env_configs.tags=[CoordSokoban]" \
  "es_manager.val.env_configs.n_groups=[4000]" \
  "agent_proxy.max_turn=10" \
  "+output.dir=results/sft_data/sokoban_1.5B_20K"

python -m ragen.llm_agent.agent_proxy \
  --config-name _2_sokoban \
  model_path=Qwen/Qwen2.5-14B-Instruct \
  "system.CUDA_VISIBLE_DEVICES=0" \
  "actor_rollout_ref.rollout.tensor_model_parallel_size=1" \
  "actor_rollout_ref.rollout.max_model_len=8192" \
  "actor_rollout_ref.rollout.gpu_memory_utilization=0.7" \
  "seed.val=2042" \
  "es_manager.val.env_groups=4000" \
  "es_manager.val.group_size=5" \
  "es_manager.val.env_configs.tags=[CoordSokoban]" \
  "es_manager.val.env_configs.n_groups=[4000]" \
  "agent_proxy.max_turn=10" \
  "+output.dir=results/sft_data/sokoban_14B_20K"