diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index d0b6249e1c33..543c8ced165a 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -939,15 +939,17 @@ def grouped_topk(hidden_states: torch.Tensor,
     else:
         raise ValueError(f"Unsupported scoring function: {scoring_func}")
 
+    num_token = scores.shape[0]
     if e_score_correction_bias is not None:
         # Store original scores before applying correction bias. We use biased
         # scores for expert selection but original scores for routing weights
         original_scores = scores
         scores = scores + e_score_correction_bias.unsqueeze(0)
-
-    num_token = scores.shape[0]
-    group_scores = scores.view(num_token, num_expert_group,
-                               -1).max(dim=-1).values  # [n, n_group]
+        group_scores = (scores.view(num_token, num_expert_group,
+                                    -1).topk(2, dim=-1)[0].sum(dim=-1))
+    else:
+        group_scores = scores.view(num_token, num_expert_group,
+                                   -1).max(dim=-1).values  # [n, n_group]
     group_idx = torch.topk(group_scores, k=topk_group, dim=-1,
                            sorted=False)[1]  # [n, top_k_group]
     group_mask = torch.zeros_like(group_scores)  # [n, n_group]
@@ -955,7 +957,8 @@ def grouped_topk(hidden_states: torch.Tensor,
     score_mask = group_mask.unsqueeze(-1).expand(
         num_token, num_expert_group,
         scores.shape[-1] // num_expert_group).reshape(num_token, -1)  # [n, e]
-    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+    tmp_scores = scores.masked_fill(~score_mask.bool(),
+                                    float("-inf"))  # [n, e]
 
     if e_score_correction_bias is not None:
         topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)[1]