From 5fdcd9b5aa99be36c7478662f5672e83ad12b12d Mon Sep 17 00:00:00 2001
From: iomgaa <iomgaaycz@gmail.com>
Date: Thu, 16 Jul 2026 07:33:16 -0400
Subject: [PATCH] feat: add Video-MME 900 training entry (config +
 self-contained script)

---
 config/train_videomme.yaml | 67 +++++++++++++++++++++++++++++
 scripts/train_videomme.sh  | 88 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 config/train_videomme.yaml
 create mode 100755 scripts/train_videomme.sh

diff --git a/config/train_videomme.yaml b/config/train_videomme.yaml
new file mode 100644
index 0000000..83380a4
--- /dev/null
+++ b/config/train_videomme.yaml
@@ -0,0 +1,67 @@
+# config/train_videomme.yaml
+# Video-MME 900 题自进化训练 —— 消费 video-split 冻结切分（global 三池）
+#
+# 数据来源: workspaces/video-split/pools.json（tier 感知 diag/val + val 功效修复）
+#   经 adhoc-baseline seed 携带进 workspace（WP2 接线）。
+# 训练前置: .env REDIS_CACHE_TTL 须为正整数（WP4 fail-loud）；见
+#   research-wiki/reviews/2026-07-16-preflight-final-review.md runbook。
+
+harness:
+  workspace_dir: "workspaces/train-videomme"
+  store_dir: store
+  mode: train
+  run_id: train_videomme_v1
+  concurrency: 24
+  max_steps: 40
+  skill_mode: auto
+  n_samples: 0
+  questions: "benchmarks/Video-MME"   # gate 指纹依赖加载全 900 题
+  skills_version: v1
+  prompts_version: v1
+  epochs: 3
+  # CE-Gate 参数（沿用 default.yaml）
+  gate_e_confirm: 20.0
+  gate_e_provisional: 3.0
+  gate_w_net_min: 2
+  gate_delta_min: 0.02
+  gate_lambda_dir: -0.642
+  gate_e_rollback: 10.0
+  gate_block: 8
+  gate_n_max: 40
+  gate_p_low: 0.05
+  gate_p_high: 0.95
+  gate_probe_quota: 0.2
+  gate_gamma_decay: 0.9
+  gate_cooldown_steps: 2
+  gate_guard_err: 0.10
+  # 进化参数
+  edit_budget_start: 5
+  edit_budget_end: 2
+  skill_update_mode: patch
+  appendix_consolidate_threshold: 6
+  # 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）
+  pool_split_mode: global
+  diag_size: 210
+  diag_correct_ratio: 0.5
+  val_size: 90
+  val_correct_ratio: 0.5
+  test_size: 600
+  test_questions: "benchmarks/Video-MME"
+  # 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
+  eval_min_per_class: 2
+  trainable_min_units: 8
+  # mini-batch
+  batch_size: 10
+  min_class_per_batch: 2
+  batch_correct_ratio: 0.5
+  momentum_samples: 20
+  early_stop_patience: 2          # epoch 粒度（WP3）：连续 2 epoch 无 best 刷新即停
+  use_slow_momentum: true
+  run_holdout_eval: true          # 逐 epoch test 四向评估（WP3 去重版：baseline 推导 + 版本备忘录）
+  # 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）
+
+embed:
+  backend: "local"
+  model_name: "BAAI/bge-base-zh-v1.5"
+  embed_dim: 768
+  device: "cuda"
diff --git a/scripts/train_videomme.sh b/scripts/train_videomme.sh
new file mode 100755
index 0000000..4e0cd31
--- /dev/null
+++ b/scripts/train_videomme.sh
@@ -0,0 +1,88 @@
+#!/usr/bin/env bash
+# ============================================================================
+# Video-MME 900 自进化训练 —— 消费 video-split 冻结切分（capstone 训练）
+# ----------------------------------------------------------------------------
+# 自包含实验记录：写死全部参数，零参可复现（GPU 卡号除外）。
+#
+#   Phase 0: 建 adhoc-baseline seed（extract infer_adhoc 去重 baseline.db +
+#            携带 video-split 冻结 pools.json / manifest；已存在则跳过）
+#   Phase 1: train --fresh --seed adhoc-baseline（3 epochs，global 冻结切分）
+#
+# 用法（长时训练，建议 tmux 便于 attach 看实时进度）:
+#   tmux new-session -d -s train_videomme "CUDA_VISIBLE_DEVICES=0 bash scripts/train_videomme.sh 2>&1 | tee logs/train_videomme.log"
+#   或直接: CUDA_VISIBLE_DEVICES=0 bash scripts/train_videomme.sh
+#
+# 日志三重保险即时输出（绝不缓存）: PYTHONUNBUFFERED=1（环境级）+ python -u（命令级）
+#   + loguru 走 stderr 同步写。tee 落盘不影响实时性。
+#
+# 前置（务必先做，见 research-wiki/reviews/2026-07-16-preflight-final-review.md）:
+#   1. .env REDIS_CACHE_TTL 为正整数（0 会被 WP4 fail-loud 拒绝启动）
+#   2. workspaces/video-split/pools.json 已冻结（build_video_split.sh 产出）
+#   3. workspaces/default/harness.db 含 infer_adhoc 基线预测
+# ============================================================================
+
+set -euo pipefail
+
+cd "$(dirname "$0")/.."
+
+export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0}"
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export PYTHONUNBUFFERED=1
+
+set -a
+source .env
+set +a
+
+PYTHON="$(conda run -n Video-Tree-TRM which python)"
+
+SEED_NAME="adhoc-baseline"
+SPLIT_DIR="workspaces/video-split"
+BASELINE_DB="workspaces/default/harness.db"
+
+# ── Phase 0: 建 seed（携带冻结 pools.json）──
+if [[ ! -d "store/seeds/${SEED_NAME}" ]]; then
+  echo "=== Phase 0: 建 seed ${SEED_NAME}（携带 video-split 冻结切分）==="
+  "${PYTHON}" -u -c "
+from pathlib import Path
+import tempfile
+from app.harness.store import extract_run_db, init_seed
+
+split_dir = Path('${SPLIT_DIR}')
+pools = split_dir / 'pools.json'
+manifest = split_dir / 'split_manifest.json'
+assert pools.exists(), f'冻结切分不存在: {pools}（先跑 build_video_split.sh）'
+
+tmp = Path(tempfile.mkdtemp()) / 'baseline.db'
+# canonical 每 question_id 取首行（902→900），对齐冻结切分口径
+extract_run_db(Path('${BASELINE_DB}'), tmp, 'infer_adhoc', dedupe_per_question=True)
+init_seed(
+    store_dir=Path('store'),
+    name='${SEED_NAME}',
+    skills_dir=Path('store/skills/v1'),
+    prompts_dir=Path('store/prompts/v1'),
+    baseline_db=tmp,
+    baseline_run_id='infer_adhoc',
+    parent=None,
+    description='Video-MME adhoc baseline + video-split 冻结三池（tier 感知 val_ratio=0.4）',
+    pools_json=pools,
+    split_manifest=manifest,
+)
+tmp.unlink()
+print('Seed created: store/seeds/${SEED_NAME}/')
+"
+else
+  echo "=== Phase 0: seed ${SEED_NAME} 已存在，跳过 ==="
+fi
+
+# ── Phase 1: Train ──
+echo "=== Phase 1: Train (3 epochs, Video-MME 900 global 冻结切分) ==="
+"${PYTHON}" -u main.py \
+  --config config/train_videomme.yaml \
+  --fresh \
+  --seed "${SEED_NAME}"
+
+echo "=== 训练完成 ==="
+echo "结果查看:"
+echo "  cat workspaces/train-videomme/analyses/final_test_eval.json"
+echo "  sqlite3 workspaces/train-videomme/harness.db 'SELECT * FROM dual_metric'"