diff --git a/tests/workers/test_distillation_policy_gradient_on_cpu.py b/tests/workers/test_distillation_policy_gradient_on_cpu.py
new file mode 100644
index 00000000000..a6abf9b7698
--- /dev/null
+++ b/tests/workers/test_distillation_policy_gradient_on_cpu.py
@@ -0,0 +1,107 @@
+# Copyright 2025 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Compare real policy-gradient distillation against a dense PPO reference."""
+
+import pytest
+import torch
+from tensordict import TensorDict
+
+from verl.trainer.distillation.losses import distillation_loss
+from verl.trainer.ppo.core_algos import compute_policy_loss_vanilla
+from verl.utils import tensordict_utils as tu
+from verl.workers.config import ActorConfig
+from verl.workers.config.distillation import DistillationConfig, DistillationLossConfig
+
+
+def _nested(rows: list[list[float]] | list[list[int]] | list[list[bool]]) -> torch.Tensor:
+    return torch.nested.nested_tensor([torch.tensor(row) for row in rows], layout=torch.jagged)
+
+
+@pytest.mark.parametrize("weights_layout", ["nested", "dense", "absent"])
+@pytest.mark.parametrize("nested_response_fields", [True, False])
+@pytest.mark.parametrize("loss_agg_mode", ["token-mean", "seq-mean-token-mean"])
+def test_policy_gradient_weights_match_dense_loss_and_gradient(
+    weights_layout: str, nested_response_fields: bool, loss_agg_mode: str
+) -> None:
+    """Unequal response lengths, masked tokens and non-unit weights preserve PPO semantics."""
+    actor_config = ActorConfig(
+        strategy="megatron", rollout_n=1, ppo_micro_batch_size_per_gpu=1, loss_agg_mode=loss_agg_mode
+    )
+    loss_config = DistillationLossConfig(loss_mode="k1", loss_max_clamp=None, use_policy_gradient=True)
+    distillation_config = DistillationConfig(distillation_loss=loss_config)
+    # Sequence lengths are 5 and 3, with prompt lengths 2 and 1. Model outputs
+    # predict the next token; response log-probs are at packed indices 1:4 and 5:7.
+    student = torch.tensor([-9.0, -0.2, -1.3, -0.4, -8.0, -0.6, -0.9, -7.0], requires_grad=True)
+    teacher = torch.tensor([-6.0, -0.4, -1.0, -0.2, -5.0, -0.8, -0.6, -4.0], requires_grad=True)
+    offsets = torch.tensor([0, 5, 8])
+    mask = _nested([[True, False, True], [True, True]])
+    old_log_probs = _nested([[-0.25, -1.1, -0.5], [-0.65, -0.7]])
+    dense_mask = torch.tensor([[True, False, True], [True, True, False]])
+    dense_old_log_probs = torch.tensor([[-0.25, -1.1, -0.5], [-0.65, -0.7, 0.0]])
+    data = TensorDict(
+        {
+            "prompts": _nested([[1, 2], [3]]),
+            "responses": _nested([[4, 5, 6], [7, 8]]),
+            "response_mask": mask if nested_response_fields else dense_mask,
+            "old_log_probs": old_log_probs if nested_response_fields else dense_old_log_probs,
+            "teacher_logprobs": torch.nested.nested_tensor_from_jagged(teacher.unsqueeze(-1), offsets),
+        },
+        batch_size=[],
+    )
+    tu.assign_non_tensor(data, dp_size=1, batch_num_tokens=4, global_batch_size=2)
+    dense_weights = torch.tensor([[1.3, 0.7, 0.0], [0.6, 1.8, 0.0]])
+    if weights_layout == "nested":
+        data["rollout_is_weights"] = _nested([[1.3, 0.7, 0.0], [0.6, 1.8]])
+    elif weights_layout == "dense":
+        data["rollout_is_weights"] = dense_weights
+    model_output = {"log_probs": torch.nested.nested_tensor_from_jagged(student, offsets)}
+    actual_loss, metrics = distillation_loss(actor_config, distillation_config, model_output, data)
+    actual_loss.backward()
+
+    # Build the reference independently of no_padding_2_padding and the weights
+    # conversion, while exercising the same real clipped policy-loss function.
+    reference_log_probs = torch.tensor([[-0.2, -1.3, -0.4], [-0.6, -0.9, 0.0]], requires_grad=True)
+    teacher_log_probs = torch.tensor([[-0.4, -1.0, -0.2], [-0.8, -0.6, 0.0]])
+    advantages = -(reference_log_probs.detach() - teacher_log_probs)
+    expected_loss, expected_metrics = compute_policy_loss_vanilla(
+        old_log_prob=dense_old_log_probs,
+        log_prob=reference_log_probs,
+        advantages=advantages,
+        response_mask=dense_mask,
+        loss_agg_mode=loss_agg_mode,
+        config=loss_config,
+        rollout_is_weights=dense_weights if weights_layout != "absent" else None,
+    )
+    expected_loss.backward()
+    torch.testing.assert_close(actual_loss, expected_loss)
+    expected_gradient = torch.zeros_like(student)
+    expected_gradient[1:4] = reference_log_probs.grad[0]
+    expected_gradient[5:7] = reference_log_probs.grad[1, :2]
+    torch.testing.assert_close(student.grad, expected_gradient)
+    assert student.grad.abs().sum() > 0
+    assert teacher.grad is None
+    for key, value in expected_metrics.items():
+        assert metrics[key.replace("actor/", "distillation/")] == pytest.approx(value)
+    if weights_layout != "absent":
+        unweighted_loss, _ = compute_policy_loss_vanilla(
+            old_log_prob=dense_old_log_probs,
+            log_prob=reference_log_probs,
+            advantages=advantages,
+            response_mask=dense_mask,
+            loss_agg_mode=loss_agg_mode,
+            config=loss_config,
+        )
+        assert not torch.isclose(actual_loss, unweighted_loss)
+        assert student.grad[3] == 0  # A valid response with zero IS weight contributes no gradient.
diff --git a/verl/trainer/distillation/losses.py b/verl/trainer/distillation/losses.py
index b939c585c34..17e7a554309 100644
--- a/verl/trainer/distillation/losses.py
+++ b/verl/trainer/distillation/losses.py
@@ -277,6 +277,8 @@ def distillation_loss(
         if response_mask.is_nested:
             response_mask = response_mask.to_padded_tensor(False)
         rollout_is_weights = data.get("rollout_is_weights", None)
+        if rollout_is_weights is not None and rollout_is_weights.is_nested:
+            rollout_is_weights = rollout_is_weights.to_padded_tensor(0.0)
         distillation_loss, pg_metrics = policy_loss_fn(
             old_log_prob=old_log_prob,
             log_prob=log_prob,