exp(dagger-deploy): 采集支持 fb_gain+safety（训练分布=部署分布）；新增 base_dag2 配置（同 base）

ml/dagger/collect.py

@@ -28,6 +28,8 @@ def main():
     ap.add_argument('--parts', type=int, default=8); ap.add_argument('--out', required=True)
     ap.add_argument('--bs', type=int, default=64)
     ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
+    ap.add_argument('--fb_gain', type=float, default=0.0, help='部署分布采集：目标反馈增益（与 rollout 一致）')
+    ap.add_argument('--safety', type=int, default=0, help='部署分布采集：是否开安全层')
     a = ap.parse_args()
     gap_min = float(os.environ.get('MIN_GAP_MM') or 3)
     os.makedirs(a.out, exist_ok=True)
@@ -41,7 +43,7 @@ def main():
     for i in range(0, len(tasks), a.bs):
         tb = tasks[i:i + a.bs]
         with torch.no_grad():
-            _, per = rollout(model, kin, tb, a.device, K=8, safety=False, seed=a.seed * 100003 + i, return_traj=True)
+            _, per = rollout(model, kin, tb, a.device, K=8, safety=bool(a.safety), seed=a.seed * 100003 + i, return_traj=True, fb_gain=a.fb_gain)
         tr = per['traj'].to(a.device)                                   # [B,T,6]
         B, Tn, _ = tr.shape
         _, T, R, _, _ = tasks_to_tensors(tb, a.device)

ml/dagger/run.sh

@@ -24,7 +24,7 @@ for ((r=1; r<=R; r++)); do
   SAMPLES="$SAMPLES $SD"
   if [ -f $OUT/done ]; then echo "[dagger] 第 $r 轮已完成，跳过"; CK=$($PY -m ml.common.ckpt pick $OUT); continue; fi
   if [ -f $D/collect.json ]; then echo "[dagger] 第 $r 轮起点已采集，跳过"
-  else echo "[dagger] 第 $r 轮：用 $CK 闭环采起点（$N 题）"; $PY -m ml.dagger.collect --ckpt $CK --samples ml/data/samples --n $N --seed $r --parts $P --out $D; fi
+  else echo "[dagger] 第 $r 轮：用 $CK 闭环采起点（$N 题）"; $PY -m ml.dagger.collect --ckpt $CK --samples ml/data/samples --n $N --seed $r --parts $P --out $D --fb_gain ${FB_GAIN:-0} --safety ${DAG_SAFETY:-0}; fi
   cat $D/collect.json
   pids=()
   for f in $D/tasks_*.jsonl; do