Nirbhayhero07/deepsentinel-overseer-small
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42a8f751349d64217097b2c01ddc861588e19206
deepsentinel-overseer-small/artifacts/train.log
ModelHub XC 42a8f75134 初始化项目，由ModelHub XC社区提供模型 
Model: Nirbhayhero07/deepsentinel-overseer-small
Source: Original Platform
2026-09-06 18:29:15 +08:00

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============================================================
DeepSentinel GRPO Training
============================================================
[1] Collecting 120 training episodes...
Collected 120 episodes
Distribution: 40 easy, 40 medium, 40 hard
[2] Evaluating heuristic baseline...
easy: avg_reward=-0.3681
medium: avg_reward=0.0049
hard: avg_reward=0.0833
[3] Loading model: Qwen/Qwen2.5-0.5B-Instruct
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The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
Model loaded: 494.0M parameters
[4] Building training dataset...
[5] Configuring GRPO trainer...
[6] Training for 250 steps...
Reward will be logged every 10 steps.
Watch for increasing reward_mean over time.
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80%|████████ | 200/250 [1:56:54<29:12, 35.04s/it]{'loss': '0', 'grad_norm': '0', 'learning_rate': '1.128e-05', 'num_tokens': '6.999e+05', 'completions/mean_length': '389.8', 'completions/min_length': '336.6', 'completions/max_length': '400', 'completions/clipped_ratio': '0.95', 'completions/mean_terminated_length': '65.95', 'completions/min_terminated_length': '56.6', 'completions/max_terminated_length': '75.3', 'rewards/reward_fn/mean': '0.06825', 'rewards/reward_fn/std': '0.8598', 'reward': '0.06825', 'reward_std': '0.8598', 'frac_reward_zero_std': '1', 'entropy': '0.8245', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.43', 'epoch': '3.667'}
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{'loss': '0', 'grad_norm': '0', 'learning_rate': '5.68e-06', 'num_tokens': '1.151e+06', 'completions/mean_length': '400', 'completions/min_length': '400', 'completions/max_length': '400', 'completions/clipped_ratio': '1', 'completions/mean_terminated_length': '0', 'completions/min_terminated_length': '0', 'completions/max_terminated_length': '0', 'rewards/reward_fn/mean': '0.082', 'rewards/reward_fn/std': '0.9346', 'reward': '0.082', 'reward_std': '0.9346', 'frac_reward_zero_std': '1', 'entropy': '0.9227', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.47', 'epoch': '6'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '4.88e-06', 'num_tokens': '1.215e+06', 'completions/mean_length': '394.4', 'completions/min_length': '355.5', 'completions/max_length': '400', 'completions/clipped_ratio': '0.975', 'completions/mean_terminated_length': '35.5', 'completions/min_terminated_length': '35.5', 'completions/max_terminated_length': '35.5', 'rewards/reward_fn/mean': '-0.08775', 'rewards/reward_fn/std': '0.8883', 'reward': '-0.08775', 'reward_std': '0.8883', 'frac_reward_zero_std': '0.975', 'entropy': '0.9981', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.42', 'epoch': '6.333'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '4.08e-06', 'num_tokens': '1.28e+06', 'completions/mean_length': '398.8', 'completions/min_length': '390.4', 'completions/max_length': '400', 'completions/clipped_ratio': '0.9875', 'completions/mean_terminated_length': '30.4', 'completions/min_terminated_length': '30.4', 'completions/max_terminated_length': '30.4', 'rewards/reward_fn/mean': '-0.03525', 'rewards/reward_fn/std': '0.8935', 'reward': '-0.03525', 'reward_std': '0.8935', 'frac_reward_zero_std': '1', 'entropy': '0.979', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.42', 'epoch': '6.667'}
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100%|██████████| 250/250 [2:26:39<00:00, 35.17s/it]{'loss': '0', 'grad_norm': '0', 'learning_rate': '3.28e-06', 'num_tokens': '1.344e+06', 'completions/mean_length': '392.1', 'completions/min_length': '337.2', 'completions/max_length': '400', 'completions/clipped_ratio': '0.9625', 'completions/mean_terminated_length': '57.2', 'completions/min_terminated_length': '57.2', 'completions/max_terminated_length': '57.2', 'rewards/reward_fn/mean': '-0.0115', 'rewards/reward_fn/std': '0.8286', 'reward': '-0.0115', 'reward_std': '0.8286', 'frac_reward_zero_std': '1', 'entropy': '0.913', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.59', 'epoch': '7'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '2.48e-06', 'num_tokens': '1.409e+06', 'completions/mean_length': '400', 'completions/min_length': '400', 'completions/max_length': '400', 'completions/clipped_ratio': '1', 'completions/mean_terminated_length': '0', 'completions/min_terminated_length': '0', 'completions/max_terminated_length': '0', 'rewards/reward_fn/mean': '0.01825', 'rewards/reward_fn/std': '0.8384', 'reward': '0.01825', 'reward_std': '0.8384', 'frac_reward_zero_std': '1', 'entropy': '0.9604', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.32', 'epoch': '7.333'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '1.68e-06', 'num_tokens': '1.474e+06', 'completions/mean_length': '397.4', 'completions/min_length': '379.6', 'completions/max_length': '400', 'completions/clipped_ratio': '0.9875', 'completions/mean_terminated_length': '19.6', 'completions/min_terminated_length': '19.6', 'completions/max_terminated_length': '19.6', 'rewards/reward_fn/mean': '-0.1123', 'rewards/reward_fn/std': '0.8594', 'reward': '-0.1123', 'reward_std': '0.8594', 'frac_reward_zero_std': '1', 'entropy': '0.9672', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.42', 'epoch': '7.667'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '8.8e-07', 'num_tokens': '1.538e+06', 'completions/mean_length': '389.5', 'completions/min_length': '316.1', 'completions/max_length': '400', 'completions/clipped_ratio': '0.9625', 'completions/mean_terminated_length': '36.1', 'completions/min_terminated_length': '36.1', 'completions/max_terminated_length': '36.1', 'rewards/reward_fn/mean': '-0.015', 'rewards/reward_fn/std': '0.9199', 'reward': '-0.015', 'reward_std': '0.9199', 'frac_reward_zero_std': '1', 'entropy': '1.013', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.59', 'epoch': '8'}
{'loss': '0', 'grad_norm': '0', 'learning_rate': '8e-08', 'num_tokens': '1.603e+06', 'completions/mean_length': '391.8', 'completions/min_length': '334.6', 'completions/max_length': '400', 'completions/clipped_ratio': '0.9625', 'completions/mean_terminated_length': '54.6', 'completions/min_terminated_length': '54.6', 'completions/max_terminated_length': '54.6', 'rewards/reward_fn/mean': '0.0445', 'rewards/reward_fn/std': '0.861', 'reward': '0.0445', 'reward_std': '0.861', 'frac_reward_zero_std': '1', 'entropy': '0.9223', 'clip_ratio/low_mean': '0', 'clip_ratio/low_min': '0', 'clip_ratio/high_mean': '0', 'clip_ratio/high_max': '0', 'clip_ratio/region_mean': '0', 'step_time': '33.55', 'epoch': '8.333'}
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{'train_runtime': '8818', 'train_samples_per_second': '0.227', 'train_steps_per_second': '0.028', 'train_loss': '-0.001472', 'epoch': '8.333'}
[7] Saving model to ./deepsentinel_model_small
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Done!
[8] Post-training evaluation...
Baseline vs Post-training (heuristic used for comparison):
easy: -0.3681 → -0.3681 (Δ=+0.0000)
medium: 0.0049 → 0.0049 (Δ=+0.0000)
hard: 0.0833 → 0.0833 (Δ=+0.0000)
Training complete!
Model saved to: ./deepsentinel_model_small
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