diff --git a/vllm_kunlun/v1/sample/ops/topk_topp_sampler.py b/vllm_kunlun/v1/sample/ops/topk_topp_sampler.py
index 4aa5012..8d904e5 100644
--- a/vllm_kunlun/v1/sample/ops/topk_topp_sampler.py
+++ b/vllm_kunlun/v1/sample/ops/topk_topp_sampler.py
@@ -37,7 +37,7 @@ class TopKTopPSampler(nn.Module):
         generators: dict[int, torch.Generator],
         k: Optional[torch.Tensor],
         p: Optional[torch.Tensor],
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """
         PyTorch-native implementation of top-k and top-p sampling.
 
@@ -58,7 +58,7 @@ class TopKTopPSampler(nn.Module):
         generators: dict[int, torch.Generator],
         k: Optional[torch.Tensor],
         p: Optional[torch.Tensor],
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """More optimized implementation for top-k and top-p sampling."""
         if (k is None and p is None) or generators:
             if generators:
@@ -158,14 +158,21 @@ def random_sample(
         if os.getenv('FAST_RANDOM_SAMPLE') == "1":
             q.uniform_()
             q = -torch.log(q)
-            q = q.clamp(min=1e-4)
+            q = q.clamp(min=1e-12)
         else:
             q.exponential_()
     if generators:
         # TODO(woosuk): This can be slow because we handle each request
         # one by one. Optimize this.
-        for i, generator in generators.items():
-            q[i].exponential_(generator=generator)
+        if os.getenv('FAST_RANDOM_SAMPLE') == "1":
+            for i, generator in generators.items():
+                q[i].uniform_(generator=generator)
+            q = -torch.log(q)
+            q = q.clamp(min=1e-12)
+        else:
+            for i, generator in generators.items():
+                q[i].exponential_(generator=generator)
+
     return probs.div_(q).argmax(dim=-1).view(-1)