feat(trainer): 添加残差块以增强模型表达能力

src/trainer/model.py

@@ -155,6 +155,7 @@ class MoEModel(nn.Module):
         )
         self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
         self.pooler = nn.AdaptiveAvgPool1d(1)
+        self.res_blocks = nn.ModuleList([ResidualBlock(self.hidden_size) for _ in range(4)])
 
         self.total_experts = 20
         self.experts = nn.ModuleList()
@@ -215,6 +216,9 @@ class MoEModel(nn.Module):
         # ----- 3. 池化量 -----
         pooled = self.pooler(encoded.transpose(1, 2)).squeeze(-1)
 
+        for block in self.res_blocks:
+            pooled = block(pooled)
+
         # ----- 4. 专家路由（硬路由）-----
         if torch.jit.is_tracing():
             # ------------------ ONNX 导出模式：条件分支（batch=1）------------------