diff --git "a/docs/logdump.txt" "b/docs/logdump.txt"
new file mode 100644--- /dev/null
+++ "b/docs/logdump.txt"
@@ -0,0 +1,3273 @@
+Hugging Face's logo
+Hugging Face
+Models
+Datasets
+Spaces
+Buckets
+new
+Docs
+Enterprise
+Pricing
+
+
+Jobs
+:
+ InosLihka
+/
+69ed8c25d70108f37acdf744
+
+Status
+
+Canceled
+Created
+
+26/04/2026, 09:23:09
+
+Hardware
+
+a10g-large
+
+Image
+
+ghcr.io/astral-sh/uv:python3.12-bookworm
+Command
+
+bash -c 'echo $LOCAL_FILES_ENCODED | xargs -n 2 bash -c '\''echo "$1" | base64 -d > "$0"'\'' && uv run '\''train_on_hf.py'\'''
+
+Environment variables
+
+FAST_MODE=1 LOCAL_FILES_ENCODED=train_on_hf.py # /// script
# requires-python = ">=3.10"
# dependencies = [
#   "torch",
#   "transformers==4.56.2",
#   "trl==0.22.2",
#   "datasets",
#   "peft",
#   "accelerate",
#   "bitsandbytes",
#   "unsloth",
#   "openenv-core",
#   "fastapi",
#   "uvicorn",
#   "pydantic",
#   "matplotlib",
#   "huggingface_hub",
# ]
# ///
"""
End-to-end training job for HF Jobs.

Submit from local machine with:
    hf jobs uv run --flavor a10g-large --secrets HF_TOKEN scripts/train_on_hf.py

What it does (no babysitting required):
  1. Clone rhythm_env from HF Space (gets latest meta-RL code from main)
  2. Generate dataset (continuous profiles, hint_fraction=0.15)
  3. Train Qwen 2.5-3B + LoRA rank 8 via GRPO (1500 steps)
  4. Run eval on all 3 conditions (discrete, in-dist, OOD)
  5. Generate all 5 plots from log_history
  6. Upload trained model + plots + eval JSON to a new HF Hub model repo

Override defaults via env vars:
    MAX_STEPS, NUM_EPISODES, LORA_RANK, BETA, MODEL_REPO

Estimated cost on a10g-large at $1.50/hr: ~$3 for 1500 steps (~2h).
"""

import json
import os
import shutil
import subprocess
import sys
from pathlib import Path

# ---------------------------------------------------------------------------
# Config (overridable via env vars)
# ---------------------------------------------------------------------------
REPO_URL = os.environ.get("REPO_URL", "https://huggingface.co/spaces/InosLihka/rhythm_env")
WORK_DIR = "/tmp/rhythm_env"
OUTPUT_DIR = "/tmp/rhythm_env/outputs/rhythmenv_meta_trained"
PLOTS_DIR = "/tmp/rhythm_env/plots"

# FAST_MODE preset: ~10-15 min iteration on A100 large.
# Use for hyperparameter sweeps and pipeline debugging.
FAST_MODE = os.environ.get("FAST_MODE", "0") == "1"

if FAST_MODE:
    # Iter 3 preset: 800 steps + 8 generations + LoRA 16 to escape mode collapse for real
    DEFAULTS = dict(MAX_STEPS=800, NUM_EPISODES=200, MAX_SAMPLES=2000,
                    NUM_GENERATIONS=8, LORA_RANK=16, BETA=0.04,
                    LEARNING_RATE=5e-5, EVAL_EPISODES=2)
else:
    DEFAULTS = dict(MAX_STEPS=2000, NUM_EPISODES=400, MAX_SAMPLES=4000,
                    NUM_GENERATIONS=8, LORA_RANK=16, BETA=0.04,
                    LEARNING_RATE=5e-5, EVAL_EPISODES=5)

MAX_STEPS = int(os.environ.get("MAX_STEPS", str(DEFAULTS["MAX_STEPS"])))
NUM_EPISODES = int(os.environ.get("NUM_EPISODES", str(DEFAULTS["NUM_EPISODES"])))
MAX_SAMPLES = int(os.environ.get("MAX_SAMPLES", str(DEFAULTS["MAX_SAMPLES"])))
NUM_GENERATIONS = int(os.environ.get("NUM_GENERATIONS", str(DEFAULTS["NUM_GENERATIONS"])))
LORA_RANK = int(os.environ.get("LORA_RANK", str(DEFAULTS["LORA_RANK"])))
BETA = float(os.environ.get("BETA", str(DEFAULTS["BETA"])))
LEARNING_RATE = float(os.environ.get("LEARNING_RATE", str(DEFAULTS["LEARNING_RATE"])))
EVAL_EPISODES = int(os.environ.get("EVAL_EPISODES", str(DEFAULTS["EVAL_EPISODES"])))

# Each iteration uploads to a unique repo if MODEL_REPO_SUFFIX is set
SUFFIX = os.environ.get("MODEL_REPO_SUFFIX", "")
DEFAULT_REPO = "InosLihka/rhythm-env-meta-trained" + (f"-{SUFFIX}" if SUFFIX else "")
MODEL_REPO = os.environ.get("MODEL_REPO", DEFAULT_REPO)

print(f"=== Run config ===")
print(f"  FAST_MODE: {FAST_MODE}")
print(f"  MAX_STEPS={MAX_STEPS}, NUM_EPISODES={NUM_EPISODES}, MAX_SAMPLES={MAX_SAMPLES}")
print(f"  NUM_GENERATIONS={NUM_GENERATIONS}, LORA_RANK={LORA_RANK}, BETA={BETA}")
print(f"  LEARNING_RATE={LEARNING_RATE}, EVAL_EPISODES={EVAL_EPISODES}")
print(f"  MODEL_REPO={MODEL_REPO}")
print()


def run(cmd: list[str], **kw):
    """Run subprocess with logging."""
    print(f"\n>>> {' '.join(cmd) if isinstance(cmd, list) else cmd}", flush=True)
    subprocess.run(cmd, check=True, **kw)


def main():
    # ---------------------------------------------------------------
    # 1. Clone the rhythm_env repo
    # ---------------------------------------------------------------
    if Path(WORK_DIR).exists():
        shutil.rmtree(WORK_DIR)
    run(["git", "clone", REPO_URL, WORK_DIR])
    os.chdir(WORK_DIR)
    sys.path.insert(0, WORK_DIR)
    sys.path.insert(0, os.path.join(WORK_DIR, "training"))

    # Verify meta-RL code is present
    dataset_py = Path("training/dataset.py").read_text()
    assert "profile_mode" in dataset_py, "Cloned repo doesn't have meta-RL code"
    print("OK: meta-RL code present in cloned repo")

    # ---------------------------------------------------------------
    # 2. Train
    # ---------------------------------------------------------------
    train_args = [
        "python", "training/train.py",
        "--max_steps", str(MAX_STEPS),
        "--num_episodes", str(NUM_EPISODES),
        "--max_samples", str(MAX_SAMPLES),
        "--num_generations", str(NUM_GENERATIONS),
        "--lora_rank", str(LORA_RANK),
        "--beta", str(BETA),
        "--learning_rate", str(LEARNING_RATE),
        "--output_dir", OUTPUT_DIR,
    ]
    run(train_args)

    # ---------------------------------------------------------------
    # 3. Eval (3 conditions: discrete-3 / in-dist / OOD)
    # ---------------------------------------------------------------
    eval_args = [
        "python", "training/inference_eval.py",
        "--model_path", OUTPUT_DIR,
        "--num_episodes", str(EVAL_EPISODES),
        "--output_file", "eval_results.json",
    ]
    run(eval_args)

    # ---------------------------------------------------------------
    # 4. Generate plots from saved log_history
    # ---------------------------------------------------------------
    Path(PLOTS_DIR).mkdir(exist_ok=True)
    log_path = os.path.join(OUTPUT_DIR, "log_history.json")
    if Path(log_path).exists():
        run(["python", "scripts/plot_from_log.py", "--log", log_path, "--out", PLOTS_DIR])
    else:
        print(f"WARNING: log_history.json not found at {log_path}")

    # ---------------------------------------------------------------
    # 5. Upload everything to HF Hub
    # ---------------------------------------------------------------
    token = os.environ.get("HF_TOKEN")
    if not token:
        print("WARNING: HF_TOKEN not set, skipping upload")
        print(f"Outputs in: {OUTPUT_DIR}")
        return

    from huggingface_hub import HfApi, login
    login(token=token)
    api = HfApi()
    api.create_repo(MODEL_REPO, exist_ok=True, repo_type="model")

    # Upload trained model + config + log_history
    api.upload_folder(
        folder_path=OUTPUT_DIR,
        repo_id=MODEL_REPO,
        repo_type="model",
        commit_message=f"Trained {MAX_STEPS}-step GRPO meta-RL agent",
    )

    # Upload eval JSON
    api.upload_file(
        path_or_fileobj="eval_results.json",
        path_in_repo="eval_results.json",
        repo_id=MODEL_REPO,
        repo_type="model",
    )

    # Upload plots if generated
    if Path(PLOTS_DIR).exists() and any(Path(PLOTS_DIR).iterdir()):
        api.upload_folder(
            folder_path=PLOTS_DIR,
            path_in_repo="plots",
            repo_id=MODEL_REPO,
            repo_type="model",
        )

    print()
    print("=" * 60)
    print("DONE")
    print(f"  Trained model: https://huggingface.co/{MODEL_REPO}")
    print(f"  Eval JSON:     https://huggingface.co/{MODEL_REPO}/blob/main/eval_results.json")
    print(f"  Plots:         https://huggingface.co/{MODEL_REPO}/tree/main/plots")
    print("=" * 60)


if __name__ == "__main__":
    main()
 MODEL_REPO_SUFFIX=iter4
+Secrets
+
+HF_TOKEN
+Logs
+===== Job started at 2026-04-26 03:53:10 =====
+
+Downloading hf-xet (4.0MiB)
+
+Downloading pydantic-core (2.0MiB)
+
+Downloading kiwisolver (1.4MiB)
+
+Downloading pyarrow (46.6MiB)
+
+Downloading transformers (11.1MiB)
+
+Downloading hf-transfer (3.4MiB)
+
+Downloading nvidia-cufile-cu12 (1.1MiB)
+
+Downloading cryptography (4.5MiB)
+
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+
+Downloading nvidia-nvjitlink-cu12 (37.4MiB)
+
+Downloading pillow (6.8MiB)
+
+Downloading pygments (1.2MiB)
+
+Downloading unsloth (63.9MiB)
+
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+
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+
+Downloading brotli (1.4MiB)
+
+Downloading fonttools (4.8MiB)
+
+Downloading triton (179.5MiB)
+
+Downloading numpy (15.9MiB)
+
+Downloading nvidia-cudnn-cu12 (674.0MiB)
+
+Downloading aiohttp (1.7MiB)
+
+Downloading nvidia-curand-cu12 (60.7MiB)
+
+Downloading nvidia-cusparselt-cu12 (273.9MiB)
+
+Downloading nvidia-cuda-nvrtc-cu12 (84.0MiB)
+
+Downloading nvidia-cuda-cupti-cu12 (9.8MiB)
+
+Downloading nvidia-nccl-cu12 (307.4MiB)
+
+Downloading tokenizers (3.1MiB)
+
+Downloading matplotlib (8.4MiB)
+
+Downloading nvidia-nvshmem-cu12 (132.7MiB)
+
+Downloading pandas (10.4MiB)
+
+Downloading xformers (3.1MiB)
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+Downloading beartype (1.3MiB)
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+Downloading torch (873.2MiB)
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+Downloading nvidia-cufft-cu12 (184.2MiB)
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+Downloading nvidia-cusolver-cu12 (255.1MiB)
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+Downloading nvidia-cublas-cu12 (566.8MiB)
+
+Downloading diffusers (4.8MiB)
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+Downloading torchao (3.1MiB)
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+Downloading cuda-bindings (11.6MiB)
+
+Downloading sentencepiece (1.3MiB)
+
+ Downloaded nvidia-cufile-cu12
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+ Downloaded pygments
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+ Downloaded brotli
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+ Downloaded networkx
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+ Downloaded tokenizers
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+ Downloaded openai
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+ Downloaded xformers
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+ Downloaded hf-transfer
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+ Downloaded hf-xet
+
+ Downloaded cryptography
+
+ Downloaded torchao
+
+ Downloaded fonttools
+
+ Downloaded diffusers
+
+ Downloaded pillow
+
+ Downloaded torchvision
+
+ Downloaded sympy
+
+ Downloaded matplotlib
+
+ Downloaded nvidia-cuda-cupti-cu12
+
+ Downloaded cuda-bindings
+
+ Downloaded transformers
+
+ Downloaded numpy
+
+ Downloaded pandas
+
+ Downloaded gradio
+
+ Downloaded nvidia-nvjitlink-cu12
+
+ Downloaded bitsandbytes
+
+ Downloaded nvidia-curand-cu12
+
+ Downloaded pyarrow
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+ Downloaded nvidia-cuda-nvrtc-cu12
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+
+ Downloaded nvidia-cublas-cu12
+
+ Downloaded nvidia-cudnn-cu12
+
+ Downloaded torch
+
+Installed 172 packages in 531ms
+
+=== Run config ===
+
+  FAST_MODE: True
+
+  MAX_STEPS=800, NUM_EPISODES=200, MAX_SAMPLES=2000
+
+  NUM_GENERATIONS=8, LORA_RANK=16, BETA=0.04
+
+  LEARNING_RATE=5e-05, EVAL_EPISODES=2
+
+  MODEL_REPO=InosLihka/rhythm-env-meta-trained-iter4
+
+
+
+>>> git clone https://huggingface.co/spaces/InosLihka/rhythm_env /tmp/rhythm_env
+
+Cloning into '/tmp/rhythm_env'...
+
+OK: meta-RL code present in cloned repo
+
+
+>>> python training/train.py --max_steps 800 --num_episodes 200 --max_samples 2000 --num_generations 8 --lora_rank 16 --beta 0.04 --learning_rate 5e-05 --output_dir /tmp/rhythm_env/outputs/rhythmenv_meta_trained
+
+============================================================
+
+Step 1: Generating training dataset (continuous profiles)
+
+============================================================
+
+Generated 2000 samples from 72 episodes (0 with profile hint, 2000 without)
+
+Dataset size: 2000
+
+
+============================================================
+
+Step 2: Loading model unsloth/Qwen2.5-3B-Instruct
+
+============================================================
+
+🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
+
+🦥 Unsloth Zoo will now patch everything to make training faster!
+
+==((====))==  Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.56.2.
+
+   \\   /|    NVIDIA A10G. Num GPUs = 1. Max memory: 22.301 GB. Platform: Linux.
+
+O^O/ \_/ \    Torch: 2.10.0+cu128. CUDA: 8.6. CUDA Toolkit: 12.8. Triton: 3.6.0
+
+\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.35. FA2 = False]
+
+ "-____-"     Free license: http://github.com/unslothai/unsloth
+
+Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
+
+
+
+model.safetensors:   0%|          | 0.00/2.36G [00:00<?, ?B/s]
+
+
+model.safetensors:   3%|▎         | 68.0M/2.36G [00:01<00:36, 63.0MB/s]
+
+
+model.safetensors:  93%|█████████▎| 2.18G/2.36G [00:02<00:00, 1.23GB/s]
+model.safetensors: 100%|██████████| 2.36G/2.36G [00:02<00:00, 879MB/s] 
+
+
+
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+tokenizer.json: 100%|██████████| 11.4M/11.4M [00:00<00:00, 53.6MB/s]
+
+unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit does not have a padding token! Will use pad_token = <|PAD_TOKEN|>.
+
+Unsloth 2026.4.8 patched 36 layers with 36 QKV layers, 36 O layers and 36 MLP layers.
+
+LoRA rank: 16, alpha: 32
+
+
+============================================================
+
+Step 3: Setting up reward functions
+
+============================================================
+
+Using: format_valid + action_legal + env_reward + belief_accuracy
+
+
+============================================================
+
+Step 4: Configuring GRPO trainer
+
+============================================================
+
+Unsloth: We now expect `per_device_train_batch_size` * `gradient_accumulation_steps` * `world_size` to be a multiple of `num_generations`.
+
+We will change the batch size of 1 to the `num_generations` of 8
+
+Using GRPOConfig with reward_weights=[0.05, 0.05, 1.5, 3.0]
+
+max_steps=800, num_generations=8, lr=5e-05, beta=0.04
+
+max_prompt_length=600, max_completion_length=32
+
+hint_fraction=0.0 (curriculum warmup)
+
+
+============================================================
+
+Step 5: Starting GRPO training
+
+============================================================
+
+==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
+
+   \\   /|    Num examples = 2,000 | Num Epochs = 2 | Total steps = 800
+
+O^O/ \_/ \    Batch size per device = 8 | Gradient accumulation steps = 4
+
+\        /    Data Parallel GPUs = 1 | Total batch size (8 x 4 x 1) = 32
+
+ "-____-"     Trainable parameters = 29,933,568 of 3,115,872,256 (0.96% trained)
+
+
+Unsloth: Will smartly offload gradients to save VRAM!
+
+
+  0%|          | 0/800 [00:00<?, ?it/s]
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+{'loss': -0.0, 'grad_norm': 36.08258819580078, 'learning_rate': 0.0, 'num_tokens': 19974.0, 'completions/mean_length': 24.1875, 'completions/min_length': 6.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.375, 'completions/mean_terminated_length': 19.5, 'completions/min_terminated_length': 6.0, 'completions/max_terminated_length': 32.0, 'rewards/format_valid/mean': -1.125, 'rewards/format_valid/std': 1.2313119173049927, 'rewards/action_legal/mean': -0.65625, 'rewards/action_legal/std': 0.4825586974620819, 'rewards/env_reward/mean': -1.874782919883728, 'rewards/env_reward/std': 1.6368685960769653, 'rewards/belief_accuracy/mean': -0.09578604996204376, 'rewards/belief_accuracy/std': 0.023837711662054062, 'reward': -3.1885948181152344, 'reward_std': 2.168053150177002, 'frac_reward_zero_std': 0.0, 'completion_length': 32.0, 'kl': -8.149072527885437e-10, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.0}
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+                                                  
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+{'loss': -0.0, 'grad_norm': 35.3685188293457, 'learning_rate': 6.25e-07, 'num_tokens': 39898.0, 'completions/mean_length': 22.625, 'completions/min_length': 7.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.40625, 'completions/mean_terminated_length': 16.210525512695312, 'completions/min_terminated_length': 7.0, 'completions/max_terminated_length': 28.0, 'rewards/format_valid/mean': -1.28125, 'rewards/format_valid/std': 1.1704527139663696, 'rewards/action_legal/mean': -0.71875, 'rewards/action_legal/std': 0.45680341124534607, 'rewards/env_reward/mean': -2.105933666229248, 'rewards/env_reward/std': 1.464045524597168, 'rewards/belief_accuracy/mean': -0.09861111640930176, 'rewards/belief_accuracy/std': 0.007856741547584534, 'reward': -3.5547335147857666, 'reward_std': 1.6820682287216187, 'frac_reward_zero_std': 0.25, 'completion_length': 32.0, 'kl': -6.402842700481415e-10, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.0}
+
+
+  0%|          | 2/800 [03:33<20:08:56, 90.90s/it]
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+{'loss': 0.0004, 'grad_norm': 22.421438217163086, 'learning_rate': 1.25e-06, 'num_tokens': 59913.0, 'completions/mean_length': 25.46875, 'completions/min_length': 10.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.53125, 'completions/mean_terminated_length': 18.066667556762695, 'completions/min_terminated_length': 10.0, 'completions/max_terminated_length': 27.0, 'rewards/format_valid/mean': -1.59375, 'rewards/format_valid/std': 0.9625025987625122, 'rewards/action_legal/mean': -0.84375, 'rewards/action_legal/std': 0.3689020276069641, 'rewards/env_reward/mean': -2.5606305599212646, 'rewards/env_reward/std': 1.0509716272354126, 'rewards/belief_accuracy/mean': -0.09691542387008667, 'rewards/belief_accuracy/std': 0.017448989674448967, 'reward': -4.253566741943359, 'reward_std': 1.3898957967758179, 'frac_reward_zero_std': 0.25, 'completion_length': 32.0, 'kl': 0.010594317922368646, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.01}
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+                                                  
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+                                                 
+
+
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+  1%|          | 5/800 [04:21<7:00:03, 31.70s/it]
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+  1%|          | 5/800 [04:21<7:00:03, 31.70s/it]
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+  1%|          | 6/800 [06:42<15:12:04, 68.92s/it]
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+  1%|          | 7/800 [06:58<11:21:57, 51.60s/it]
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+  1%|          | 7/800 [06:58<11:21:57, 51.60s/it]
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+  1%|          | 8/800 [07:14<8:51:13, 40.24s/it] 
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+  1%|          | 8/800 [07:14<8:51:13, 40.24s/it]
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+
+  1%|          | 9/800 [07:30<7:10:24, 32.65s/it]
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+  1%|          | 9/800 [07:30<7:10:24, 32.65s/it]
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+  1%|▏         | 10/800 [07:49<6:11:26, 28.21s/it]
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+  1%|▏         | 10/800 [07:49<6:11:26, 28.21s/it]
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+  1%|▏         | 11/800 [08:05<5:21:49, 24.47s/it]
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+  1%|▏         | 11/800 [08:05<5:21:49, 24.47s/it]
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+  2%|▏         | 12/800 [08:20<4:47:14, 21.87s/it]
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+  2%|▏         | 13/800 [08:36<4:23:12, 20.07s/it]
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+                                                  
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+  2%|▏         | 13/800 [08:36<4:23:12, 20.07s/it]
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+  2%|▏         | 14/800 [08:52<4:06:25, 18.81s/it]
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+                                                  
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+
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+  2%|▏         | 14/800 [08:52<4:06:25, 18.81s/it]
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+
+  2%|▏         | 15/800 [09:08<3:54:36, 17.93s/it]
+
+
+                                                  
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+
+  2%|▏         | 15/800 [09:08<3:54:36, 17.93s/it]
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+  2%|▏         | 16/800 [09:24<3:46:23, 17.33s/it]
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+
+
+  2%|▏         | 16/800 [09:24<3:46:23, 17.33s/it]
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+
+  2%|▏         | 17/800 [09:40<3:40:32, 16.90s/it]
+
+
+                                                  
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+
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+  2%|▏         | 17/800 [09:40<3:40:32, 16.90s/it]
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+
+  2%|▏         | 18/800 [09:56<3:36:22, 16.60s/it]
+
+
+                                                  
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+  2%|▏         | 18/800 [09:56<3:36:22, 16.60s/it]
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+  2%|▏         | 19/800 [10:14<3:42:38, 17.10s/it]
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+                                                  
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+  3%|▎         | 21/800 [10:46<3:34:10, 16.50s/it]
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+  3%|▎         | 22/800 [11:02<3:31:37, 16.32s/it]
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+  3%|▎         | 23/800 [11:18<3:29:43, 16.20s/it]
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+  3%|▎         | 23/800 [11:18<3:29:43, 16.20s/it]
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+  3%|▎         | 24/800 [11:34<3:28:19, 16.11s/it]
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+  3%|▎         | 25/800 [11:50<3:27:16, 16.05s/it]
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+  3%|▎         | 25/800 [11:50<3:27:16, 16.05s/it]
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+  3%|▎         | 26/800 [12:06<3:26:35, 16.01s/it]
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+  3%|▎         | 26/800 [12:06<3:26:35, 16.01s/it]
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+  3%|▎         | 27/800 [12:21<3:25:56, 15.98s/it]
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+  3%|▎         | 27/800 [12:21<3:25:56, 15.98s/it]
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+  4%|▎         | 28/800 [12:37<3:25:27, 15.97s/it]
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+  4%|▎         | 28/800 [12:37<3:25:27, 15.97s/it]
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+  4%|▎         | 29/800 [12:53<3:24:55, 15.95s/it]
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+                                                  
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+  4%|▎         | 29/800 [12:53<3:24:55, 15.95s/it]
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+  4%|▍         | 30/800 [13:09<3:24:29, 15.93s/it]
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+  4%|▍         | 30/800 [13:09<3:24:29, 15.93s/it]
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+  4%|▍         | 31/800 [13:25<3:24:09, 15.93s/it]
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+                                                  
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+  4%|▍         | 31/800 [13:25<3:24:09, 15.93s/it]
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+  4%|▍         | 32/800 [13:41<3:23:51, 15.93s/it]
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+                                                  
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+  4%|▍         | 32/800 [13:41<3:23:51, 15.93s/it]
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+  4%|▍         | 33/800 [13:57<3:23:33, 15.92s/it]
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+                                                  
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+  4%|▍         | 33/800 [13:57<3:23:33, 15.92s/it]
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+  4%|▍         | 34/800 [14:13<3:23:13, 15.92s/it]
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+                                                  
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+  4%|▍         | 36/800 [14:45<3:22:47, 15.93s/it]
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+  5%|▍         | 38/800 [15:17<3:22:16, 15.93s/it]
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+  5%|▍         | 39/800 [15:33<3:22:04, 15.93s/it]
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+  5%|▌         | 40/800 [15:51<3:30:43, 16.64s/it]
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+  5%|▌         | 40/800 [15:51<3:30:43, 16.64s/it]
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+  5%|▌         | 41/800 [16:09<3:36:46, 17.14s/it]
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+  5%|▌         | 42/800 [16:25<3:31:59, 16.78s/it]
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+  5%|▌         | 43/800 [16:42<3:31:40, 16.78s/it]
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+  5%|▌         | 43/800 [16:42<3:31:40, 16.78s/it]
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+  6%|▌         | 44/800 [16:58<3:28:05, 16.51s/it]
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+  6%|▌         | 44/800 [16:58<3:28:05, 16.51s/it]
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+  6%|▌         | 45/800 [17:14<3:25:49, 16.36s/it]
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+  6%|▌         | 46/800 [17:30<3:23:50, 16.22s/it]
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+  6%|▌         | 46/800 [17:30<3:23:50, 16.22s/it]
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+  6%|▌         | 47/800 [17:46<3:22:30, 16.14s/it]
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+  6%|▌         | 48/800 [18:02<3:21:37, 16.09s/it]
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+  6%|▌         | 48/800 [18:02<3:21:37, 16.09s/it]
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+  6%|▌         | 49/800 [18:18<3:21:01, 16.06s/it]
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+  6%|▌         | 49/800 [18:18<3:21:01, 16.06s/it]
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+  6%|▋         | 50/800 [18:33<3:20:21, 16.03s/it]
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+  6%|▋         | 51/800 [18:49<3:19:51, 16.01s/it]
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+  6%|▋         | 51/800 [18:49<3:19:51, 16.01s/it]
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+  6%|▋         | 52/800 [19:05<3:19:23, 15.99s/it]
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+  7%|▋         | 53/800 [19:21<3:18:53, 15.98s/it]
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+  7%|▋         | 53/800 [19:21<3:18:53, 15.98s/it]
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+  7%|▋         | 54/800 [19:40<3:27:17, 16.67s/it]
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+  7%|▋         | 54/800 [19:40<3:27:17, 16.67s/it]
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+  7%|▋         | 55/800 [19:56<3:24:22, 16.46s/it]
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+                                                  
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+  7%|▋         | 55/800 [19:56<3:24:22, 16.46s/it]
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+  7%|▋         | 56/800 [20:12<3:22:17, 16.31s/it]
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+  7%|▋         | 56/800 [20:12<3:22:17, 16.31s/it]
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+  7%|▋         | 57/800 [20:28<3:20:41, 16.21s/it]
+
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+                                                  
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+  7%|▋         | 57/800 [20:28<3:20:41, 16.21s/it]
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+  7%|▋         | 58/800 [20:43<3:19:30, 16.13s/it]
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+                                                  
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+  7%|▋         | 58/800 [20:43<3:19:30, 16.13s/it]
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+  7%|▋         | 59/800 [21:02<3:27:18, 16.79s/it]
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+  7%|▋         | 59/800 [21:02<3:27:18, 16.79s/it]
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+  8%|▊         | 60/800 [21:18<3:24:02, 16.54s/it]
+
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+                                                  
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+  8%|▊         | 60/800 [21:18<3:24:02, 16.54s/it]
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+  8%|▊         | 61/800 [21:36<3:30:19, 17.08s/it]
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+                                                  
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+  8%|▊         | 61/800 [21:36<3:30:19, 17.08s/it]
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+  8%|▊         | 62/800 [21:52<3:26:05, 16.75s/it]
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+  8%|▊         | 62/800 [21:52<3:26:05, 16.75s/it]
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+  8%|▊         | 63/800 [22:08<3:23:00, 16.53s/it]
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+                                                  
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+  8%|▊         | 63/800 [22:08<3:23:00, 16.53s/it]
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+  8%|▊         | 64/800 [22:24<3:20:40, 16.36s/it]
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+                                                  
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+  8%|▊         | 64/800 [22:24<3:20:40, 16.36s/it]
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+                                                  
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+  8%|▊         | 65/800 [22:42<3:27:51, 16.97s/it]
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+  8%|▊         | 66/800 [22:58<3:24:07, 16.69s/it]
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+                                                  
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+  8%|▊         | 66/800 [22:58<3:24:07, 16.69s/it]
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+
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+  8%|▊         | 67/800 [23:17<3:29:52, 17.18s/it]
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+                                                  
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+  8%|▊         | 67/800 [23:17<3:29:52, 17.18s/it]
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+{'loss': 0.0339, 'grad_norm': 18.394432067871094, 'learning_rate': 4.1875e-05, 'num_tokens': 1349694.0, 'completions/mean_length': 21.6875, 'completions/min_length': 9.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.4375, 'completions/mean_terminated_length': 13.666666984558105, 'completions/min_terminated_length': 9.0, 'completions/max_terminated_length': 26.0, 'rewards/format_valid/mean': 0.40625, 'rewards/format_valid/std': 1.180742621421814, 'rewards/action_legal/mean': -0.1875, 'rewards/action_legal/std': 0.3965577781200409, 'rewards/env_reward/mean': -0.1496710181236267, 'rewards/env_reward/std': 1.4231793880462646, 'rewards/belief_accuracy/mean': -0.06207277625799179, 'rewards/belief_accuracy/std': 0.08056068420410156, 'reward': -0.3997873067855835, 'reward_std': 1.4436144828796387, 'frac_reward_zero_std': 0.0, 'completion_length': 32.0, 'kl': 0.8472457826137543, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.14}
+
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+  8%|▊         | 68/800 [23:35<3:33:44, 17.52s/it]
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+                                                  
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+  8%|▊         | 68/800 [23:35<3:33:44, 17.52s/it]
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+{'loss': 0.0341, 'grad_norm': 13.612622261047363, 'learning_rate': 4.25e-05, 'num_tokens': 1369694.0, 'completions/mean_length': 25.0, 'completions/min_length': 8.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.5, 'completions/mean_terminated_length': 18.0, 'completions/min_terminated_length': 8.0, 'completions/max_terminated_length': 32.0, 'rewards/format_valid/mean': 0.0625, 'rewards/format_valid/std': 1.2296733856201172, 'rewards/action_legal/mean': -0.25, 'rewards/action_legal/std': 0.4399413466453552, 'rewards/env_reward/mean': -0.6420800089836121, 'rewards/env_reward/std': 1.4343847036361694, 'rewards/belief_accuracy/mean': -0.08176907896995544, 'rewards/belief_accuracy/std': 0.07760892063379288, 'reward': -1.2178021669387817, 'reward_std': 1.2687938213348389, 'frac_reward_zero_std': 0.0, 'completion_length': 32.0, 'kl': 0.8533681333065033, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.14}
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+  9%|▊         | 69/800 [23:51<3:27:39, 17.04s/it]
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+                                                  
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+  9%|▊         | 69/800 [23:51<3:27:39, 17.04s/it]
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+  9%|▉         | 70/800 [24:07<3:23:30, 16.73s/it]
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+                                                  
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+  9%|▉         | 70/800 [24:07<3:23:30, 16.73s/it]
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+  9%|▉         | 71/800 [24:23<3:20:31, 16.50s/it]
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+                                                  
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+  9%|▉         | 71/800 [24:23<3:20:31, 16.50s/it]
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+  9%|▉         | 72/800 [24:39<3:18:27, 16.36s/it]
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+                                                  
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+  9%|▉         | 72/800 [24:39<3:18:27, 16.36s/it]
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+  9%|▉         | 73/800 [24:55<3:16:45, 16.24s/it]
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+                                                  
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+  9%|▉         | 73/800 [24:55<3:16:45, 16.24s/it]
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+  9%|▉         | 74/800 [25:11<3:15:34, 16.16s/it]
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+
+                                                  
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+  9%|▉         | 74/800 [25:11<3:15:34, 16.16s/it]
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+  9%|▉         | 75/800 [25:27<3:14:42, 16.11s/it]
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+
+                                                  
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+  9%|▉         | 75/800 [25:27<3:14:42, 16.11s/it]
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+ 10%|▉         | 76/800 [25:43<3:13:59, 16.08s/it]
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+                                                  
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+ 10%|▉         | 76/800 [25:43<3:13:59, 16.08s/it]
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+ 10%|▉         | 77/800 [26:01<3:21:44, 16.74s/it]
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+                                                  
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+ 10%|▉         | 77/800 [26:01<3:21:44, 16.74s/it]
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+ 10%|▉         | 78/800 [26:17<3:18:47, 16.52s/it]
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+                                                  
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+ 10%|▉         | 78/800 [26:17<3:18:47, 16.52s/it]
+
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+
+
+ 10%|▉         | 79/800 [26:33<3:16:44, 16.37s/it]
+
+
+                                                  
+
+
+
+ 10%|▉         | 79/800 [26:33<3:16:44, 16.37s/it]
+
+
+ 10%|█         | 80/800 [26:49<3:14:58, 16.25s/it]
+
+
+                                                  
+
+
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+
+
+ 10%|█         | 80/800 [26:49<3:14:58, 16.25s/it]
+
+
+ 10%|█         | 81/800 [27:05<3:13:35, 16.16s/it]
+
+
+                                                  
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+
+
+ 10%|█         | 81/800 [27:05<3:13:35, 16.16s/it]
+
+{'loss': 0.0487, 'grad_norm': 3.0294620990753174, 'learning_rate': 4.993055555555556e-05, 'num_tokens': 1628635.0, 'completions/mean_length': 21.8125, 'completions/min_length': 8.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.375, 'completions/mean_terminated_length': 15.699999809265137, 'completions/min_terminated_length': 8.0, 'completions/max_terminated_length': 31.0, 'rewards/format_valid/mean': 0.796875, 'rewards/format_valid/std': 0.24949544668197632, 'rewards/action_legal/mean': 0.0, 'rewards/action_legal/std': 0.0, 'rewards/env_reward/mean': 0.20791909098625183, 'rewards/env_reward/std': 0.3105705976486206, 'rewards/belief_accuracy/mean': -0.12021970748901367, 'rewards/belief_accuracy/std': 0.11214939504861832, 'reward': -0.008936740458011627, 'reward_std': 0.3372165560722351, 'frac_reward_zero_std': 0.0, 'completion_length': 32.0, 'kl': 1.2180950939655304, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.16}
+
+
+ 10%|█         | 82/800 [27:21<3:12:36, 16.10s/it]
+
+
+                                                  
+
+
+
+ 10%|█         | 82/800 [27:21<3:12:36, 16.10s/it]
+
+{'loss': 0.0597, 'grad_norm': 3.1383140087127686, 'learning_rate': 4.986111111111111e-05, 'num_tokens': 1648544.0, 'completions/mean_length': 22.15625, 'completions/min_length': 9.0, 'completions/max_length': 32.0, 'completions/clipped_ratio': 0.40625, 'completions/mean_terminated_length': 15.421052932739258, 'completions/min_terminated_length': 9.0, 'completions/max_terminated_length': 30.0, 'rewards/format_valid/mean': 0.671875, 'rewards/format_valid/std': 0.5478605628013611, 'rewards/action_legal/mean': -0.03125, 'rewards/action_legal/std': 0.1767766922712326, 'rewards/env_reward/mean': -0.018333418294787407, 'rewards/env_reward/std': 0.6250711679458618, 'rewards/belief_accuracy/mean': -0.148574098944664, 'rewards/belief_accuracy/std': 0.08691953122615814, 'reward': -0.4411911964416504, 'reward_std': 0.6577557921409607, 'frac_reward_zero_std': 0.0, 'completion_length': 32.0, 'kl': 1.4920355677604675, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.17}
+
+
+ 10%|█         | 83/800 [27:37<3:11:54, 16.06s/it]
+
+
+                                                  
+
+
+
+ 10%|█         | 83/800 [27:37<3:11:54, 16.06s/it]
+
+
+ 10%|█         | 84/800 [27:53<3:11:37, 16.06s/it]
+
+
+                                                  
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+
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+
+ 10%|█         | 84/800 [27:53<3:11:37, 16.06s/it]
+
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+
+
+ 11%|█         | 85/800 [28:09<3:11:06, 16.04s/it]
+
+
+                                                  
+
+
+
+ 11%|█         | 85/800 [28:09<3:11:06, 16.04s/it]
+
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+
+
+ 11%|█         | 86/800 [28:25<3:10:39, 16.02s/it]
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+
+                                                  
+
+
+
+ 11%|█         | 86/800 [28:25<3:10:39, 16.02s/it]
+
+
+ 11%|█         | 87/800 [28:41<3:10:26, 16.03s/it]
+
+
+                                                  
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+
+ 11%|█         | 87/800 [28:41<3:10:26, 16.03s/it]
+
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+
+
+ 11%|█         | 88/800 [28:57<3:10:16, 16.04s/it]
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+
+                                                  
+
+
+
+ 11%|█         | 88/800 [28:57<3:10:16, 16.04s/it]
+
+
+ 11%|█         | 89/800 [29:13<3:09:50, 16.02s/it]
+
+
+                                                  
+
+
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+
+ 11%|█         | 89/800 [29:13<3:09:50, 16.02s/it]
+
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+
+
+ 11%|█▏        | 90/800 [29:29<3:09:20, 16.00s/it]
+
+
+                                                  
+
+
+
+ 11%|█▏        | 90/800 [29:29<3:09:20, 16.00s/it]
+
+
+ 11%|█▏        | 91/800 [29:45<3:09:06, 16.00s/it]
+
+
+                                                  
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+
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+
+ 11%|█▏        | 91/800 [29:45<3:09:06, 16.00s/it]
+
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+
+
+ 12%|█▏        | 92/800 [30:01<3:08:55, 16.01s/it]
+
+
+                                                  
+
+
+
+ 12%|█▏        | 92/800 [30:01<3:08:55, 16.01s/it]
+
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+
+
+ 12%|█▏        | 93/800 [30:17<3:08:33, 16.00s/it]
+
+
+                                                  
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+
+
+ 12%|█▏        | 93/800 [30:17<3:08:33, 16.00s/it]
+
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+ 12%|█▏        | 94/800 [30:33<3:08:08, 15.99s/it]
+
+
+                                                  
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+                                                  
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+ 12%|█▏        | 98/800 [31:37<3:07:10, 16.00s/it]
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+ 12%|█▏        | 99/800 [31:53<3:06:47, 15.99s/it]
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+ 12%|█▎        | 100/800 [32:09<3:06:34, 15.99s/it]
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+ 13%|█▎        | 101/800 [32:25<3:06:26, 16.00s/it]
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+ 13%|█▎        | 102/800 [32:41<3:06:19, 16.02s/it]
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+ 13%|█▎        | 102/800 [32:41<3:06:19, 16.02s/it]
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+ 13%|█▎        | 103/800 [32:57<3:05:57, 16.01s/it]
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+ 13%|█▎        | 103/800 [32:57<3:05:57, 16.01s/it]
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+ 13%|█▎        | 104/800 [33:16<3:14:07, 16.74s/it]
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+                                                   
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+ 13%|█▎        | 104/800 [33:16<3:14:07, 16.74s/it]
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+ 13%|█▎        | 105/800 [33:32<3:11:20, 16.52s/it]
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+                                                   
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+ 13%|█▎        | 105/800 [33:32<3:11:20, 16.52s/it]
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+ 13%|█▎        | 106/800 [36:12<11:31:21, 59.77s/it]
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+                                                    
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+ 13%|█▎        | 106/800 [36:12<11:31:21, 59.77s/it]
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+ 13%|█▎        | 107/800 [36:31<9:06:47, 47.34s/it] 
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+ 13%|█▎        | 107/800 [36:31<9:06:47, 47.34s/it]
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+                                                   
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+ 14%|█▎        | 108/800 [36:47<7:17:27, 37.93s/it]
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+                                                   
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+ 14%|█▎        | 109/800 [37:03<6:00:56, 31.34s/it]
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+ 14%|█▍        | 111/800 [37:35<4:29:45, 23.49s/it]
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+ 14%|█▍        | 112/800 [37:51<4:03:35, 21.24s/it]
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+ 14%|█▍        | 113/800 [38:07<3:45:08, 19.66s/it]
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+ 14%|█▍        | 114/800 [38:23<3:32:21, 18.57s/it]
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+ 14%|█▍        | 114/800 [38:23<3:32:21, 18.57s/it]
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+ 14%|█▍        | 115/800 [38:38<3:23:03, 17.79s/it]
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+ 14%|█▍        | 115/800 [38:38<3:23:03, 17.79s/it]
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+ 14%|█▍        | 116/800 [38:54<3:16:40, 17.25s/it]
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+                                                   
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+ 14%|█▍        | 116/800 [38:54<3:16:40, 17.25s/it]
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+ 15%|█▍        | 117/800 [39:10<3:11:54, 16.86s/it]
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+                                                   
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+ 15%|█▍        | 117/800 [39:10<3:11:54, 16.86s/it]
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+ 15%|█▍        | 118/800 [39:29<3:16:33, 17.29s/it]
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+                                                   
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+ 15%|█▍        | 118/800 [39:29<3:16:33, 17.29s/it]
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+ 15%|█▍        | 119/800 [39:45<3:11:45, 16.90s/it]
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+                                                   
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+ 15%|█▍        | 119/800 [39:45<3:11:45, 16.90s/it]
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+ 15%|█▌        | 120/800 [40:01<3:08:16, 16.61s/it]
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+                                                   
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+ 15%|█▌        | 120/800 [40:01<3:08:16, 16.61s/it]
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+ 15%|█▌        | 121/800 [40:17<3:05:52, 16.43s/it]
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+                                                   
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+ 15%|█▌        | 121/800 [40:17<3:05:52, 16.43s/it]
+
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+ 15%|█▌        | 122/800 [40:33<3:04:01, 16.28s/it]
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+                                                   
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+ 15%|█▌        | 122/800 [40:33<3:04:01, 16.28s/it]
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+ 15%|█▌        | 123/800 [40:49<3:02:43, 16.19s/it]
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+                                                   
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+ 15%|█▌        | 123/800 [40:49<3:02:43, 16.19s/it]
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+ 16%|█▌        | 125/800 [41:20<3:00:48, 16.07s/it]
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+ 16%|█▌        | 126/800 [41:36<3:00:12, 16.04s/it]
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+ 16%|█▌        | 127/800 [41:52<2:59:49, 16.03s/it]
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+                                                   
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+ 16%|█▌        | 128/800 [42:08<2:59:12, 16.00s/it]
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+ 16%|█▌        | 129/800 [42:24<2:58:50, 15.99s/it]
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+ 16%|█▌        | 129/800 [42:24<2:58:50, 15.99s/it]
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+ 16%|█▋        | 130/800 [45:14<11:34:30, 62.19s/it]
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+ 16%|█▋        | 130/800 [45:14<11:34:30, 62.19s/it]
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+ 16%|█▋        | 131/800 [47:26<15:26:17, 83.08s/it]
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+                                                    
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+ 16%|█▋        | 131/800 [47:26<15:26:17, 83.08s/it]
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+ 16%|█▋        | 132/800 [49:33<17:50:00, 96.11s/it]
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+                                                    
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+ 16%|█▋        | 132/800 [49:33<17:50:00, 96.11s/it]
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+ 17%|█▋        | 133/800 [49:51<13:28:57, 72.77s/it]
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+                                                    
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+ 17%|█▋        | 133/800 [49:51<13:28:57, 72.77s/it]
+
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+ 17%|█▋        | 134/800 [52:36<18:35:02, 100.45s/it]
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+                                                     
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+ 17%|█▋        | 134/800 [52:36<18:35:02, 100.45s/it]
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+ 17%|█▋        | 135/800 [54:42<19:59:14, 108.20s/it]
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+                                                     
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+ 17%|█▋        | 135/800 [54:42<19:59:14, 108.20s/it]
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+ 17%|█▋        | 136/800 [54:58<14:51:17, 80.54s/it] 
+
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+                                                    
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+ 17%|█▋        | 136/800 [54:58<14:51:17, 80.54s/it]
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+ 17%|█▋        | 137/800 [55:14<11:15:56, 61.17s/it]
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+                                                    
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+ 17%|█▋        | 137/800 [55:14<11:15:56, 61.17s/it]
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+ 17%|█▋        | 138/800 [55:30<8:45:32, 47.63s/it] 
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+ 17%|█▋        | 138/800 [55:30<8:45:32, 47.63s/it]
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+ 18%|█▊        | 148/800 [58:10<3:03:37, 16.90s/it]
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+ 19%|█▉        | 152/800 [59:17<3:02:37, 16.91s/it]
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+ 19%|█▉        | 152/800 [59:17<3:02:37, 16.91s/it]
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+ 19%|█▉        | 153/800 [59:33<2:59:16, 16.62s/it]
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+                                                   
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+                                                     
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+                                                     
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\ No newline at end of file