diff --git a/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py b/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
index 9bcf629b8034..e1f483919b00 100644
--- a/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
+++ b/vllm/v1/worker/gpu/spec_decode/rejection_sampler.py
@@ -3,11 +3,14 @@
 import torch
 
 from vllm.triton_utils import tl, triton
+from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.worker.gpu.input_batch import InputBatch
 from vllm.v1.worker.gpu.metrics.logits import get_num_nans
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
+from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
 from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.sampler import Sampler
+from vllm.v1.worker.gpu.sample.states import NO_LOGPROBS
 
 
 @triton.jit
@@ -418,6 +421,26 @@ def probabilistic_rejection_sample(
     return sampled, rejected_steps + 1
 
 
+@triton.jit
+def _flatten_sampled_kernel(
+    # [num_logits]
+    flat_sampled_ptr,
+    # [num_reqs, num_speculative_steps + 1]
+    sampled_ptr,
+    sampled_stride,
+    # [num_reqs]
+    num_sampled_ptr,
+    # [num_reqs + 1]
+    cu_num_logits_ptr,
+):
+    req_idx = tl.program_id(0)
+    start_idx = tl.load(cu_num_logits_ptr + req_idx)
+    num_sampled = tl.load(num_sampled_ptr + req_idx)
+    for i in range(num_sampled):
+        token_id = tl.load(sampled_ptr + req_idx * sampled_stride + i)
+        tl.store(flat_sampled_ptr + start_idx + i, token_id)
+
+
 class RejectionSampler:
     def __init__(
         self,
@@ -429,6 +452,40 @@ def __init__(
         self.num_speculative_steps = num_speculative_steps
         self.use_strict_rejection_sampling = use_strict_rejection_sampling
 
+    def _get_logprobs_tensors(
+        self,
+        input_batch: InputBatch,
+        sampled: torch.Tensor,
+        num_sampled: torch.Tensor,
+        logits: torch.Tensor,
+    ) -> LogprobsTensors | None:
+        max_num_logprobs = self.sampler.sampling_states.max_num_logprobs(
+            input_batch.idx_mapping_np
+        )
+        if max_num_logprobs == NO_LOGPROBS:
+            return None
+
+        num_reqs = input_batch.cu_num_logits.shape[0] - 1
+        num_logits = logits.shape[0]
+        flat_sampled = torch.zeros(
+            num_logits, dtype=sampled.dtype, device=sampled.device
+        )
+        _flatten_sampled_kernel[(num_reqs,)](
+            flat_sampled,
+            sampled,
+            sampled.stride(0),
+            num_sampled,
+            input_batch.cu_num_logits,
+            num_warps=1,
+        )
+        expanded_logits = num_logits != input_batch.idx_mapping.shape[0]
+        return compute_topk_logprobs(
+            logits,
+            max_num_logprobs,
+            flat_sampled,
+            input_batch.cu_num_logits_np.tolist() if expanded_logits else None,
+        )
+
     def __call__(
         self,
         logits: torch.Tensor,
@@ -460,8 +517,6 @@ def __call__(
                 draft_sampled,
                 input_batch.expanded_local_pos,
             )
-            # TODO (TheEpicDolphin): Return logprobs for sampled token ids.
-            logprobs_tensors = None
             sampled, num_sampled = probabilistic_rejection_sample(
                 processed_logits,
                 draft_logits,
@@ -475,6 +530,14 @@ def __call__(
                 self.sampler.sampling_states.seeds.gpu,
                 self.num_speculative_steps,
             )
+            logprobs_tensors = self._get_logprobs_tensors(
+                input_batch,
+                sampled,
+                num_sampled,
+                processed_logits
+                if self.sampler.logprobs_mode == "processed_logprobs"
+                else logits,
+            )
 
         return SamplerOutput(
             sampled_token_ids=sampled,