diff --git a/rl_engine/testing/__init__.py b/rl_engine/testing/__init__.py
index 42be8c1..6b6a600 100644
--- a/rl_engine/testing/__init__.py
+++ b/rl_engine/testing/__init__.py
@@ -3,6 +3,7 @@
 
 """Testing helpers for RL-shaped kernel validation."""
 
+from .logprob_parity import compare_selected_logprob_layouts, make_padded_batch_layout
 from .reference_ops import (
     active_token_count,
     compute_policy_ratio,
@@ -17,8 +18,10 @@
 __all__ = [
     "SyntheticRLKernelBatch",
     "active_token_count",
+    "compare_selected_logprob_layouts",
     "compute_policy_ratio",
     "compute_reference_kl",
+    "make_padded_batch_layout",
     "make_synthetic_rl_kernel_batch",
     "masked_mean",
     "masked_sum",
diff --git a/rl_engine/testing/logprob_parity.py b/rl_engine/testing/logprob_parity.py
new file mode 100644
index 0000000..c52b4fc
--- /dev/null
+++ b/rl_engine/testing/logprob_parity.py
@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+import torch
+
+from rl_engine.testing.reference_ops import selected_logprobs_reference, summarize_kernel_drift
+
+
+def make_padded_batch_layout(
+    logits: torch.Tensor,
+    token_ids: torch.Tensor,
+    mask: torch.Tensor,
+    *,
+    destination_rows: torch.Tensor,
+    padded_batch_size: Optional[int] = None,
+    pad_token_id: int = 0,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Place completion rows into a larger padded batch layout."""
+
+    if logits.ndim < 2:
+        raise ValueError("logits must have at least batch and vocab dimensions")
+    if logits.shape[:-1] != token_ids.shape:
+        raise ValueError("logits leading shape must match token_ids shape")
+    if mask.shape != token_ids.shape:
+        raise ValueError("mask shape must match token_ids shape")
+
+    vocab_size = int(logits.shape[-1])
+    if not 0 <= int(pad_token_id) < vocab_size:
+        raise ValueError("pad_token_id must be within the logits vocabulary range")
+
+    source_batch = int(logits.shape[0])
+    rows = destination_rows.to(device=logits.device, dtype=torch.long).reshape(-1)
+    if rows.numel() != source_batch:
+        raise ValueError("destination_rows must contain one destination per source row")
+    if rows.numel() and int(rows.min().item()) < 0:
+        raise ValueError("destination_rows must be non-negative")
+    if rows.unique().numel() != rows.numel():
+        raise ValueError("destination_rows must not contain duplicates")
+
+    resolved_batch = int(padded_batch_size) if padded_batch_size is not None else source_batch
+    if rows.numel() and int(rows.max().item()) >= resolved_batch:
+        raise ValueError("destination_rows contains a row outside padded_batch_size")
+    if resolved_batch < source_batch:
+        raise ValueError("padded_batch_size must be at least the source batch size")
+
+    out_shape = (resolved_batch,) + tuple(logits.shape[1:])
+    token_shape = (resolved_batch,) + tuple(token_ids.shape[1:])
+
+    padded_logits = torch.zeros(out_shape, device=logits.device, dtype=logits.dtype)
+    padded_token_ids = torch.full(
+        token_shape,
+        int(pad_token_id),
+        device=token_ids.device,
+        dtype=token_ids.dtype,
+    )
+    padded_mask = torch.zeros(token_shape, device=mask.device, dtype=torch.bool)
+
+    padded_logits[rows] = logits
+    padded_token_ids[rows] = token_ids
+    padded_mask[rows] = mask.to(dtype=torch.bool)
+    return padded_logits, padded_token_ids, padded_mask
+
+
+def compare_selected_logprob_layouts(
+    reference_logits: torch.Tensor,
+    reference_token_ids: torch.Tensor,
+    reference_mask: torch.Tensor,
+    candidate_logits: torch.Tensor,
+    candidate_token_ids: torch.Tensor,
+    candidate_mask: torch.Tensor,
+    *,
+    candidate_rows: torch.Tensor,
+    output_dtype: torch.dtype = torch.float32,
+) -> dict[str, Any]:
+    """Compare selected logprobs for identical rows under different batch layouts."""
+
+    reference = selected_logprobs_reference(
+        reference_logits,
+        reference_token_ids,
+        mask=reference_mask,
+        output_dtype=output_dtype,
+    )
+    candidate = selected_logprobs_reference(
+        candidate_logits,
+        candidate_token_ids,
+        mask=candidate_mask,
+        output_dtype=output_dtype,
+    )
+    rows = candidate_rows.to(device=candidate.device, dtype=torch.long).reshape(-1)
+    if rows.numel() != int(reference.shape[0]):
+        raise ValueError("candidate_rows must contain one candidate row per reference row")
+    if rows.numel() and int(rows.min().item()) < 0:
+        raise ValueError("candidate_rows must be non-negative")
+    if rows.unique().numel() != rows.numel():
+        raise ValueError("candidate_rows must not contain duplicates")
+    if rows.numel() and int(rows.max().item()) >= int(candidate.shape[0]):
+        raise ValueError("candidate_rows contains a row outside the candidate batch")
+
+    restored = candidate[rows]
+    return summarize_kernel_drift(restored, reference, reference_mask)
diff --git a/tests/test_logprob_parity.py b/tests/test_logprob_parity.py
new file mode 100644
index 0000000..30be584
--- /dev/null
+++ b/tests/test_logprob_parity.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+import pytest
+import torch
+
+from rl_engine.testing import (
+    compare_selected_logprob_layouts,
+    make_padded_batch_layout,
+    selected_logprobs_reference,
+    summarize_kernel_drift,
+)
+
+
+def _case(*, device="cpu", dtype=torch.float32):
+    generator = torch.Generator(device=device).manual_seed(123)
+    logits = torch.randn(4, 5, 17, device=device, dtype=dtype, generator=generator)
+    token_ids = torch.randint(0, 17, (4, 5), device=device, generator=generator)
+    mask = torch.tensor(
+        [
+            [True, True, False, True, False],
+            [True, False, True, True, True],
+            [False, True, True, False, True],
+            [True, True, True, False, False],
+        ],
+        device=device,
+        dtype=torch.bool,
+    )
+    return logits, token_ids, mask
+
+
+def test_selected_logprob_is_invariant_to_batch_position():
+    logits, token_ids, mask = _case()
+    row_order = torch.tensor([2, 0, 3, 1])
+
+    base = selected_logprobs_reference(logits, token_ids, mask=mask)
+    shuffled = selected_logprobs_reference(
+        logits[row_order],
+        token_ids[row_order],
+        mask=mask[row_order],
+    )
+    restored = torch.empty_like(base)
+    restored[row_order] = shuffled
+
+    summary = summarize_kernel_drift(restored, base, mask)
+    assert summary["active_count"] == int(mask.sum().item())
+    assert summary["max_abs_error"] == 0.0
+    assert summary["mean_abs_error"] == 0.0
+
+
+def test_selected_logprob_is_invariant_to_padding_layout():
+    logits, token_ids, mask = _case()
+    destination_rows = torch.tensor([4, 0, 2, 5])
+
+    padded_logits, padded_token_ids, padded_mask = make_padded_batch_layout(
+        logits,
+        token_ids,
+        mask,
+        destination_rows=destination_rows,
+        padded_batch_size=6,
+    )
+
+    summary = compare_selected_logprob_layouts(
+        logits,
+        token_ids,
+        mask,
+        padded_logits,
+        padded_token_ids,
+        padded_mask,
+        candidate_rows=destination_rows,
+    )
+
+    assert summary["active_count"] == int(mask.sum().item())
+    assert summary["max_abs_error"] == 0.0
+    assert summary["mean_abs_error"] == 0.0
+
+
+def test_make_padded_batch_layout_rejects_out_of_range_pad_token_id():
+    logits, token_ids, mask = _case()
+
+    with pytest.raises(ValueError, match="pad_token_id"):
+        make_padded_batch_layout(
+            logits,
+            token_ids,
+            mask,
+            destination_rows=torch.tensor([0, 1, 2, 3]),
+            padded_batch_size=4,
+            pad_token_id=logits.shape[-1],
+        )
+
+
+@pytest.mark.parametrize(
+    "candidate_rows",
+    [
+        torch.tensor([0, 1, 1, 3]),
+        torch.tensor([0, -1, 2, 3]),
+        torch.tensor([0, 1, 2, 6]),
+        torch.tensor([0, 1, 2]),
+    ],
+)
+def test_selected_logprob_layout_compare_rejects_bad_candidate_rows(candidate_rows):
+    logits, token_ids, mask = _case()
+    padded_logits, padded_token_ids, padded_mask = make_padded_batch_layout(
+        logits,
+        token_ids,
+        mask,
+        destination_rows=torch.tensor([0, 1, 2, 3]),
+        padded_batch_size=4,
+    )
+
+    with pytest.raises(ValueError):
+        compare_selected_logprob_layouts(
+            logits,
+            token_ids,
+            mask,
+            padded_logits,
+            padded_token_ids,
+            padded_mask,
+            candidate_rows=candidate_rows,
+        )
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+def test_selected_logprob_padding_layout_cuda_dtype_sweep(dtype):
+    logits, token_ids, mask = _case(device="cuda", dtype=dtype)
+    destination_rows = torch.tensor([1, 4, 0, 3], device="cuda")
+
+    padded_logits, padded_token_ids, padded_mask = make_padded_batch_layout(
+        logits,
+        token_ids,
+        mask,
+        destination_rows=destination_rows,
+        padded_batch_size=5,
+    )
+
+    summary = compare_selected_logprob_layouts(
+        logits,
+        token_ids,
+        mask,
+        padded_logits,
+        padded_token_ids,
+        padded_mask,
+        candidate_rows=destination_rows,
+        output_dtype=torch.float32,
+    )
+
+    assert summary["active_count"] == int(mask.sum().item())
+    assert summary["max_abs_error"] == 0.0
+    assert summary["mean_abs_error"] == 0.0