fix(model): 移除梯度 NaN 检查，直接执行优化器步骤

src/trainer/model.py

@@ -496,17 +496,8 @@ class MoEModel(nn.Module):
                     scaler.unscale_(optimizer)
                     torch.nn.utils.clip_grad_norm_(self.parameters(), clip_grad_norm)
 
-                    has_nan = False
-                    for p in self.parameters():
-                        if p.grad is not None and torch.isnan(p.grad).any():
-                            has_nan = True
-                            break
-
-                    if not has_nan:
                     scaler.step(optimizer)
                     scaler.update()
-                    else:
-                        logger.warning("NaN detected, skipping step.")
                     optimizer.zero_grad()
                     batch_loss_sum += loss.item() * grad_accum_steps
                     if global_step % eval_frequency == 0: