diff --git a/ginka/train_pretrain.py b/ginka/train_pretrain.py
index f27e903..9e304aa 100644
--- a/ginka/train_pretrain.py
+++ b/ginka/train_pretrain.py
@@ -50,6 +50,9 @@ VQ_DIM_FF = 512
 VQ_BETA   = 0.5
 VQ_GAMMA  = 0.0
 
+# Focal Loss
+FOCAL_GAMMA = 2.0   # focal loss 聚焦参数（越大越关注难例/稀有类别）
+
 # 解码头超参（与编码器对称：同等层数和 FFN 宽度）
 DH_NHEAD   = 8    # Cross-Attention 头数（VQ_D_Z=128 可被 8 整除）
 DH_DIM_FF  = 512  # FFN 隐层维度（与编码器 VQ_DIM_FF 一致）
@@ -68,6 +71,24 @@ os.makedirs("result/pretrain", exist_ok=True)
 
 disable_tqdm = not sys.stdout.isatty()
 
+# ---------------------------------------------------------------------------
+# Focal Loss
+# ---------------------------------------------------------------------------
+def focal_loss(
+    logits: torch.Tensor,
+    targets: torch.Tensor,
+    gamma: float = FOCAL_GAMMA,
+) -> torch.Tensor:
+    """
+    多分类 Focal Loss（mean 归约）：FL = -(1 - p_t)^gamma * log(p_t)
+
+    相比 CE，对已被正确分类的高置信度样本施加更小的权重，
+    迫使模型关注难分类的稀有 tile（门/怪/资源等）。
+    """
+    ce = F.cross_entropy(logits, targets, reduction='none')
+    pt = torch.exp(-ce)
+    return ((1.0 - pt) ** gamma * ce).mean()
+
 # ---------------------------------------------------------------------------
 # 简单数据集：仅返回 raw_map，无子集划分，无掩码
 # ---------------------------------------------------------------------------
@@ -246,11 +267,9 @@ def train():
             # 1. 编码
             z_q, _, _, vq_loss, commit_loss, entropy_loss = model_vq(raw_map)
 
-            # 2. 解码→全图重建
+            # 2. 解码→全图重建（focal loss 缓解墙壁/空地主导问题）
             logits = decode_head(z_q)                     # [B, H*W, C]
-            ce_loss = F.cross_entropy(
-                logits.permute(0, 2, 1), raw_map          # [B, C, H*W] vs [B, H*W]
-            )
+            ce_loss = focal_loss(logits.permute(0, 2, 1), raw_map)
 
             # 3. 总损失（重建 + VQ 正则）
             loss = ce_loss + vq_loss
diff --git a/ginka/train_vq.py b/ginka/train_vq.py
index e17328c..2f0bc50 100644
--- a/ginka/train_vq.py
+++ b/ginka/train_vq.py
@@ -42,7 +42,7 @@ MASK_TOKEN       = 15
 GENERATE_STEP    = 18    # 推理时 MaskGIT 迭代步数
 MAP_SIZE         = 13 * 13
 MAP_H = MAP_W    = 13
-LABEL_SMOOTHING  = 0.0
+FOCAL_GAMMA      = 2.0   # focal loss 聚焦参数（越大越关注难例/稀有类别）
 WALL_MASK_RATIO  = 0.8
 
 # VQ-VAE 超参
@@ -107,6 +107,36 @@ def parse_arguments():
                              "适用于预训练权重加载后的热身阶段。")
     return parser.parse_args()
 
+# ---------------------------------------------------------------------------
+# Focal Loss
+# ---------------------------------------------------------------------------
+def focal_loss(
+    logits: torch.Tensor,
+    targets: torch.Tensor,
+    gamma: float = FOCAL_GAMMA,
+    reduction: str = 'none',
+) -> torch.Tensor:
+    """
+    多分类 Focal Loss：FL = -(1 - p_t)^gamma * log(p_t)
+
+    相比 CE，对已被正确分类的高置信度样本施加更小的权重，
+    迫使模型关注难分类的稀有 tile（门/怪/资源等）。
+
+    Args:
+        logits:    [B, C, *]  未经 softmax 的原始预测
+        targets:   [B, *]     整数类别标签
+        gamma:     聚焦参数，0 时退化为标准 CE
+        reduction: 'none' | 'mean' | 'sum'
+    """
+    ce = F.cross_entropy(logits, targets, reduction='none')   # [B, *]
+    pt = torch.exp(-ce)                                        # 正确类的预测概率
+    fl = (1.0 - pt) ** gamma * ce
+    if reduction == 'mean':
+        return fl.mean()
+    if reduction == 'sum':
+        return fl.sum()
+    return fl   # 'none'
+
 # ---------------------------------------------------------------------------
 # MaskGIT 推理（cosine schedule 迭代解码）
 # ---------------------------------------------------------------------------
@@ -367,10 +397,7 @@ def validate(
         logits = model_mg(masked_map, z_q, struct_cond=struct_cond_b)
         mask   = (masked_map == MASK_TOKEN)
 
-        ce_loss   = F.cross_entropy(
-            logits.permute(0, 2, 1), target_map,
-            reduction='none', label_smoothing=LABEL_SMOOTHING
-        )
+        ce_loss   = focal_loss(logits.permute(0, 2, 1), target_map)
         masked_ce = (ce_loss * mask).sum() / (mask.sum() + 1e-6)
         val_loss_total += (masked_ce + vq_loss).item()
         val_steps      += 1
@@ -623,12 +650,9 @@ def train():
             struct_cond = batch["struct_cond"].to(device)  # [B, 4]
             logits = model_mg(masked_map, z_q, struct_cond=struct_cond)  # [B, 169, C]
 
-            # 3. 只对被 mask 的位置计算 CE loss
+            # 3. 只对被 mask 的位置计算 focal loss（缓解墙壁/空地主导问题）
             mask   = (masked_map == MASK_TOKEN)           # [B, 169] bool
-            ce_loss = F.cross_entropy(
-                logits.permute(0, 2, 1), target_map,
-                reduction='none', label_smoothing=LABEL_SMOOTHING
-            )
+            ce_loss   = focal_loss(logits.permute(0, 2, 1), target_map)
             masked_ce = (ce_loss * mask).sum() / (mask.sum() + 1e-6)
 
             # 4. z 一致性约束（方案 A）：将 MaskGIT 的 logits 经温度平滑后