diff --git a/vllm_ascend/sample/rejection_sampler.py b/vllm_ascend/sample/rejection_sampler.py
index f048dcce..695ae649 100644
--- a/vllm_ascend/sample/rejection_sampler.py
+++ b/vllm_ascend/sample/rejection_sampler.py
@@ -90,7 +90,7 @@ def rejection_sample(
     # [num_tokens, vocab_size]
     draft_probs: torch.Tensor | None,
     # [num_tokens, vocab_size]
-    target_probs: torch.Tensor,
+    target_logits: torch.Tensor,
     # [batch_size, 1]
     bonus_token_ids: torch.Tensor,
     sampling_metadata: SamplingMetadata,
@@ -98,17 +98,17 @@ def rejection_sample(
     assert draft_token_ids.ndim == 1
     assert draft_probs is None or draft_probs.ndim == 2
     assert cu_num_draft_tokens.ndim == 1
-    assert target_probs.ndim == 2
+    assert target_logits.ndim == 2
 
     batch_size = len(num_draft_tokens)
     num_tokens = draft_token_ids.shape[0]
-    vocab_size = target_probs.shape[-1]
-    device = target_probs.device
+    vocab_size = target_logits.shape[-1]
+    device = target_logits.device
     assert draft_token_ids.is_contiguous()
     assert draft_probs is None or draft_probs.is_contiguous()
-    assert target_probs.is_contiguous()
+    assert target_logits.is_contiguous()
     assert bonus_token_ids.is_contiguous()
-    assert target_probs.shape == (num_tokens, vocab_size)
+    assert target_logits.shape == (num_tokens, vocab_size)
 
     # When num_speculative_tokens>=3, using block verify.
     using_block_verify = max_spec_len >= 3
@@ -129,7 +129,7 @@ def rejection_sample(
         grid, block_size = cal_grid_and_block_size(batch_size)
     if not sampling_metadata.all_random:
         # Rejection sampling for greedy sampling requests.
-        target_argmax = target_probs.argmax(dim=-1)
+        target_argmax = target_logits.argmax(dim=-1)
         if HAS_TRITON:
             rejection_greedy_sample_with_triton(
                 output_token_ids,
@@ -165,6 +165,10 @@ def rejection_sample(
         if sampling_metadata.all_greedy:
             return output_token_ids
 
+    # Compute probability distribution from target logits.
+    target_probs = target_logits.softmax(dim=-1, dtype=torch.float32)
+    assert target_probs.is_contiguous()
+
     # Generate uniform probabilities for rejection sampling.
     # [num_tokens]
     uniform_probs = generate_uniform_probs(