diff --git a/csrc/libtorch_stable/persistent_topk.cuh b/csrc/libtorch_stable/persistent_topk.cuh
index 85618feeb8..91717f53b2 100644
--- a/csrc/libtorch_stable/persistent_topk.cuh
+++ b/csrc/libtorch_stable/persistent_topk.cuh
@@ -661,7 +661,7 @@ __device__ void radix_topk(const float* __restrict__ row_input,
                            uint32_t* shared_scalars, uint32_t* shared_ordered,
                            RadixRowState* state, uint32_t cta_in_group,
                            uint32_t ctas_per_group, int& barrier_phase,
-                           uint32_t iter, uint32_t tx) {
+                           uint32_t radix_iter, uint32_t tx) {
   const uint32_t my_chunk_end = (my_chunk_start + chunk_size < seq_len)
                                     ? my_chunk_start + chunk_size
                                     : seq_len;
@@ -718,7 +718,7 @@ __device__ void radix_topk(const float* __restrict__ row_input,
 
   // -- Stage 2: 4 rounds of radix select --
   for (uint32_t round = 0; round < 4; round++) {
-    const uint32_t global_round = iter * 4 + round;
+    const uint32_t global_round = radix_iter * 4 + round;
     const uint32_t shift = 24 - round * 8;
     const uint32_t prefix = shared_scalars[0];
     const uint32_t remaining_k = shared_scalars[1];
@@ -898,6 +898,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
   RadixRowState* state = &params.row_states[group_id];
 
   int barrier_phase = 0;
+  uint32_t radix_iter = 0;
   const uint32_t total_iters = (params.num_rows + num_groups - 1) / num_groups;
 
   for (uint32_t iter = 0; iter < total_iters; iter++) {
@@ -930,7 +931,8 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
     radix_topk<TopK, VEC_SIZE>(
         row_input, row_output, seq_len, my_chunk_start, chunk_size,
         local_histogram, suffix_sum, shared_scalars, shared_ordered, state,
-        cta_in_group, ctas_per_group, barrier_phase, iter, tx);
+        cta_in_group, ctas_per_group, barrier_phase, radix_iter, tx);
+    radix_iter++;
   }
 }
 
diff --git a/tests/kernels/test_top_k_per_row.py b/tests/kernels/test_top_k_per_row.py
index 3a1ad0f0d2..dba247bbdb 100644
--- a/tests/kernels/test_top_k_per_row.py
+++ b/tests/kernels/test_top_k_per_row.py
@@ -918,6 +918,48 @@ def test_workspace_topk(test_config: dict, top_k: int, backend: str) -> None:
     )
 
 
+@pytest.mark.skipif(not current_platform.is_cuda(), reason="This test requires CUDA")
+@torch.inference_mode()
+def test_persistent_topk_reused_group_after_short_row() -> None:
+    """A short row must not advance a group's radix histogram ring."""
+    torch.set_default_device("cuda:0")
+    set_random_seed(0)
+
+    top_k = 2048
+    long_seq_len = 32769
+    radix = 256
+    fixed_smem = ((radix + radix + 5) * 4 + 15) & ~15
+    props = torch.cuda.get_device_properties(0)
+    max_smem = props.shared_memory_per_block_optin
+    if max_smem <= props.shared_memory_per_multiprocessor // 2:
+        pytest.skip("Cannot force one persistent_topk CTA per SM")
+
+    max_chunk = ((max_smem - fixed_smem) // 4 // 4) * 4
+    ctas_per_group = max(
+        (props.multi_processor_count - 1 + 9) // 10,
+        (long_seq_len + max_chunk - 1) // max_chunk,
+    )
+    if ctas_per_group >= props.multi_processor_count:
+        pytest.skip("Not enough SMs to construct a reused CTA group")
+
+    stride = ctas_per_group * max_chunk
+    num_groups = max(1, (props.multi_processor_count - 1) // ctas_per_group)
+    num_rows = 3 * num_groups
+    lengths = torch.full((num_rows,), top_k, dtype=torch.int32, device="cuda")
+    target_row = 2 * num_groups
+    lengths[0] = long_seq_len
+    lengths[num_groups] = long_seq_len - 1
+    lengths[target_row] = long_seq_len
+
+    logits = torch.randn(num_rows, stride, dtype=torch.float32, device="cuda")
+    indices = torch.empty((num_rows, top_k), dtype=torch.int32, device="cuda")
+    _run_topk_backend("persistent_topk", logits, lengths, indices, top_k, stride)
+    torch.accelerator.synchronize()
+
+    expected = logits[target_row, :long_seq_len].topk(top_k).indices
+    assert set(indices[target_row].cpu().tolist()) == set(expected.cpu().tolist())
+
+
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="This test requires CUDA")
 @pytest.mark.parametrize("top_k", [512, 2048])
 @pytest.mark.parametrize("backend", WORKSPACE_TOPK_BACKENDS)