diff --git a/fastvideo2/train/gates/capture_train_main.py b/fastvideo2/train/gates/capture_train_main.py
index dd0a4ebd3..f25bc4d59 100644
--- a/fastvideo2/train/gates/capture_train_main.py
+++ b/fastvideo2/train/gates/capture_train_main.py
@@ -33,9 +33,9 @@ import sys
 
 MODE = sys.argv[1] if len(sys.argv) > 1 and not sys.argv[1].startswith("-") else "finetune"
 VSA = MODE in ("vsa", "vsa_dmd2")
-DMD2 = MODE in ("dmd2", "vsa_dmd2")
+DMD2 = MODE in ("dmd2", "vsa_dmd2", "qad")
 os.environ["FASTVIDEO_ATTENTION_BACKEND"] = ("VIDEO_SPARSE_ATTN" if VSA else
-                                             "ATTN_QAT_TRAIN" if MODE == "qat" else "FLASH_ATTN")
+                                             "ATTN_QAT_TRAIN" if MODE in ("qat", "qad") else "FLASH_ATTN")
 os.environ.setdefault("WANDB_MODE", "offline")
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 
@@ -116,7 +116,7 @@ def main() -> None:
                  "--dmd_denoising_steps", "1000,757,522",
                  "--simulate_generator_forward",
                  "--generator_update_interval", "2",
-                 "--real_score_guidance_scale", "3.5",
+                 "--real_score_guidance_scale", "2.0" if MODE == "qad" else "3.5",
                  "--min_timestep_ratio", "0.02", "--max_timestep_ratio", "0.98",
                  "--flow_shift", "8.0"]
         argv[argv.index("5e-5")] = "2e-6"  # learning_rate per the shipped recipe
diff --git a/fastvideo2/train/gates/train_anchor.py b/fastvideo2/train/gates/train_anchor.py
index fc4db47d6..3a8e3578e 100644
--- a/fastvideo2/train/gates/train_anchor.py
+++ b/fastvideo2/train/gates/train_anchor.py
@@ -162,8 +162,9 @@ def run_dmd2(mode: str = "dmd2") -> int:
         steps = json.load(f)
     device = "cuda"
     is_vsa = mode == "vsa_dmd2"
-    # student: VSA blocks + FastWan weights; teacher/critic: DENSE blocks +
-    # base weights (main's scorers are dense-built and score via plain flash)
+    is_qad = mode == "qad"
+    # student: VSA/QAT blocks per recipe; teacher/critic: DENSE blocks + base
+    # weights (main's scorers are dense-built / loader-gated back to flash)
     card = FASTWAN_T2V_1_3B if is_vsa else WAN21_T2V_1_3B
     base_root = resolve_weights(WAN21_T2V_1_3B)
 
@@ -175,9 +176,14 @@ def run_dmd2(mode: str = "dmd2") -> int:
         student = WanModelFVVSA.from_pretrained(resolve_weights(card),
                                                 torch_dtype=torch.float32,
                                                 subfolder="transformer").to(device)
+    elif is_qad:
+        from fastvideo2.wan21.model_fv import WanModelFVQAT
+        student = WanModelFVQAT.from_pretrained(base_root, torch_dtype=torch.float32,
+                                                subfolder="transformer").to(device)
     else:
         student = load_dense()
-    step_ctx = DMD2Step(student, load_dense(), load_dense())
+    step_ctx = DMD2Step(student, load_dense(), load_dense(),
+                        guidance=2.0 if is_qad else 3.5)
     pz = np.load(os.path.join(gold, "params.npz"))
     uncond = torch.from_numpy(pz["neg_embeds"]).to(device, torch.bfloat16)
 
@@ -282,4 +288,4 @@ def run_dmd2(mode: str = "dmd2") -> int:
 if __name__ == "__main__":
     args = [a for a in sys.argv[1:] if not a.startswith("-")]
     mode = args[0] if args else "finetune"
-    sys.exit(run_dmd2(mode) if mode in ("dmd2", "vsa_dmd2") else run(mode=mode))
+    sys.exit(run_dmd2(mode) if mode in ("dmd2", "vsa_dmd2", "qad") else run(mode=mode))