zhangsiyuan
52ba43f08d
- Module C: BC+PPO training v5/v6 done; eval results in experiments/eval_intervention_v{5,6}.json
- Reward: v5 label-aligned constrained reward (code/src/rl/reward.py)
- Ablations: Module B (history_r, response_only, full) + Module C (wo_category_reward)
- SOTA baselines: WildGuard and ShieldGemma2b eval scripts and results
- Paper: update sections 05–08 (Module B/C description, experiments table, discussion)
- Docs: add record.md (change log), update state.md and exp.md; retire change.md
- Tools: add html-to-ppt utilities and run_shieldgemma2b.sh
- Configs: add ablation YAML configs for Module B and C
- Cleanup: remove stale reference/ PNG screenshots
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
52 lines
1.1 KiB
YAML
52 lines
1.1 KiB
YAML
model:
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name: "/root/siton-data-2849d4ce327c4ccfb233ce33868fe7fe/zsy/macbert-large"
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hidden_size: 1024
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num_heads: 8
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dropout: 0.1
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use_lora: false
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data:
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train_path: "data/processed/CompanionRisk-Bench/train.jsonl"
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val_path: "data/processed/CompanionRisk-Bench/dev.jsonl"
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test_path: "data/processed/CompanionRisk-Bench/test.jsonl"
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max_persona_len: 128
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max_context_len: 512
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max_response_len: 256
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max_history_turns: 5
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num_workers: 4
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ablation_mode: "response_only" # 消融：仅 Response 流，persona/context 均置空
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training:
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epochs: 10
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per_gpu_batch_size: 16
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gradient_accumulation_steps: 2
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lr: 2e-5
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warmup_steps: 100
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weight_decay: 0.01
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gradient_clip: 1.0
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eval_steps: 100
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mixed_precision: "bf16"
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seed: 42
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loss_weights:
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binary: 1.0
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level: 1.0
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primary: 1.0
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fine: 2.0
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fine_training:
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use_pos_weight: true
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risky_only: true
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evaluation:
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binary_threshold: 0.5
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fine_threshold: 0.4
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logging:
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project: "CompanionGuard-RL"
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run_name: "detector-abl-response-only"
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use_wandb: false
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output:
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checkpoint_dir: "checkpoints/detector_abl_response_only"
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