68 lines
2.2 KiB
YAML
68 lines
2.2 KiB
YAML
# config/train_videomme.yaml
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# Video-MME 900 题自进化训练 —— 消费 video-split 冻结切分（global 三池）
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#
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# 数据来源: workspaces/video-split/pools.json（tier 感知 diag/val + val 功效修复）
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# 经 adhoc-baseline seed 携带进 workspace（WP2 接线）。
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# 训练前置: .env REDIS_CACHE_TTL 须为正整数（WP4 fail-loud）；见
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# research-wiki/reviews/2026-07-16-preflight-final-review.md runbook。
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harness:
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workspace_dir: "workspaces/train-videomme"
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store_dir: store
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mode: train
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run_id: train_videomme_v1
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concurrency: 24
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max_steps: 40
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skill_mode: auto
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n_samples: 0
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questions: "benchmarks/Video-MME" # gate 指纹依赖加载全 900 题
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skills_version: v1
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prompts_version: v1
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epochs: 3
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# CE-Gate 参数（沿用 default.yaml）
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gate_e_confirm: 20.0
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gate_e_provisional: 3.0
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gate_w_net_min: 2
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gate_delta_min: 0.02
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gate_lambda_dir: -0.642
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gate_e_rollback: 10.0
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gate_block: 8
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gate_n_max: 40
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gate_p_low: 0.05
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gate_p_high: 0.95
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gate_probe_quota: 0.2
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gate_gamma_decay: 0.9
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gate_cooldown_steps: 2
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gate_guard_err: 0.10
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# 进化参数
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edit_budget_start: 5
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edit_budget_end: 2
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skill_update_mode: patch
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appendix_consolidate_threshold: 6
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# 池配置 —— global 冻结切分（diag/val/test 尺寸由 pools.json 冻结，以下采样旋钮加载时忽略）
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pool_split_mode: global
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diag_size: 210
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diag_correct_ratio: 0.5
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val_size: 90
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val_correct_ratio: 0.5
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test_size: 600
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test_questions: "benchmarks/Video-MME"
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# 可训练性预检（WP3）：val 单元 < eval_min_per_class 或 非test单元 < trainable_min_units 的题型剔除
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eval_min_per_class: 2
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trainable_min_units: 8
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# mini-batch
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batch_size: 10
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min_class_per_batch: 2
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batch_correct_ratio: 0.5
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momentum_samples: 20
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early_stop_patience: 2 # epoch 粒度（WP3）：连续 2 epoch 无 best 刷新即停
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use_slow_momentum: true
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run_holdout_eval: true # 逐 epoch test 四向评估（WP3 去重版：baseline 推导 + 版本备忘录）
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# 全 12 题型（不指定 task_types 子集，避免 I-4 语义偏差；微型类由预检自动剔除）
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embed:
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backend: "local"
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model_name: "BAAI/bge-base-zh-v1.5"
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embed_dim: 768
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device: "cuda"
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