diff --git a/skyrl/backends/skyrl_train/workers/worker.py b/skyrl/backends/skyrl_train/workers/worker.py
index ed1a3e74b6..6ee8025d0c 100644
--- a/skyrl/backends/skyrl_train/workers/worker.py
+++ b/skyrl/backends/skyrl_train/workers/worker.py
@@ -768,6 +768,15 @@ def forward_backward(
                 experience, microbatch_weight, loss_fn=loss_fn, loss_fn_config=loss_fn_config
             )
 
+            # Skip fully-padding microbatches: their loss_fn_outputs are dummy entries
+            # for samples that don't exist, and their metrics (clip_ratio=0,
+            # policy_entropy=0, ...) are meaningless and would drag down the
+            # mean-reduced metrics. Summed metrics (e.g. policy_loss) are unaffected
+            # since padding contributes 0, but excluding them here keeps both
+            # reductions correct.
+            if experience.metadata and experience.metadata.get("is_padding_batch", False):
+                continue
+
             # Extract loss_fn_outputs before reduce_metrics (it's not a scalar metric)
             if "loss_fn_outputs" in metrics:
                 all_loss_fn_outputs.extend(metrics.pop("loss_fn_outputs"))
@@ -1296,6 +1305,11 @@ def forward_backward(self, data: TrainingInputBatch) -> WorkerOutput:
                 metrics = self._forward_backward_micro(experience)
                 self._micro_batches_accumulated += 1
 
+            # Skip fully-padding microbatches: their all-zero metrics (e.g. critic_loss=0)
+            # would drag down the mean-reduced metrics. Mirrors PolicyWorkerBase.forward_backward.
+            if experience.metadata and experience.metadata.get("is_padding_batch", False):
+                continue
+
             for k, v in metrics.items():
                 all_metrics[k].append(v)
 
diff --git a/tests/backends/skyrl_train/workers/test_forward_backward_padding_metrics.py b/tests/backends/skyrl_train/workers/test_forward_backward_padding_metrics.py
new file mode 100644
index 0000000000..fbbd4cf125
--- /dev/null
+++ b/tests/backends/skyrl_train/workers/test_forward_backward_padding_metrics.py
@@ -0,0 +1,139 @@
+"""
+CPU-only tests that PolicyWorkerBase / CriticWorkerBase.forward_backward exclude
+fully-padding microbatches from metric aggregation, mirroring the Megatron-side
+behavior (megatron_worker.py).
+
+uv run --isolated --extra skyrl-train --extra dev pytest tests/backends/skyrl_train/workers/test_forward_backward_padding_metrics.py
+"""
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+
+from skyrl.backends.skyrl_train.training_batch import TrainingInputBatch
+from skyrl.backends.skyrl_train.workers.worker import CriticWorkerBase, PolicyWorkerBase
+from skyrl.backends.skyrl_train.workers.worker_utils import TokenBasedBatchIterator
+
+MAX_TOKENS_PER_MICROBATCH = 15
+
+
+def _make_batch(seq_lens, num_actions=4):
+    """Dummy TrainingInputBatch with variable sequence lengths."""
+    batch_size = len(seq_lens)
+    max_seq_len = max(seq_lens)
+
+    sequences = torch.zeros((batch_size, max_seq_len), dtype=int, device="cpu")
+    attention_mask = torch.zeros((batch_size, max_seq_len), dtype=int, device="cpu")
+    for i, seq_len in enumerate(seq_lens):
+        sequences[i, :seq_len] = torch.randint(0, 100, (seq_len,), dtype=int, device="cpu")
+        attention_mask[i, :seq_len] = 1
+
+    data = TrainingInputBatch(
+        {
+            "sequences": sequences,
+            "attention_mask": attention_mask,
+            "action_log_probs": 0.4 * torch.ones((batch_size, num_actions), device="cpu"),
+            "base_action_log_probs": 0.3 * torch.ones((batch_size, num_actions), device="cpu"),
+            "values": 0.5 * torch.ones((batch_size, num_actions), device="cpu"),
+            "returns": 0.5 * torch.ones((batch_size, num_actions), device="cpu"),
+            "advantages": 0.6 * torch.ones((batch_size, num_actions), device="cpu"),
+            "loss_mask": torch.ones((batch_size, num_actions), dtype=int, device="cpu"),
+            "response_mask": torch.ones((batch_size, num_actions), dtype=int, device="cpu"),
+        }
+    )
+    data.metadata = {"response_length": num_actions}
+    return data
+
+
+@pytest.fixture
+def force_one_padding_microbatch(monkeypatch):
+    """Force TokenBasedBatchIterator to add exactly one padding microbatch, as if another
+    DP rank had packed one more microbatch (dist is not initialized in CPU tests, so
+    _sync_num_microbatches would otherwise just return the local count)."""
+    original = TokenBasedBatchIterator._sync_num_microbatches
+
+    def one_extra(self):
+        return original(self) + 1
+
+    monkeypatch.setattr(TokenBasedBatchIterator, "_sync_num_microbatches", one_extra)
+
+
+def _identity_all_reduce(d, op=None, group=None):
+    return dict(d)
+
+
+def test_policy_padding_microbatch_excluded_from_metrics(force_one_padding_microbatch):
+    """Mean-reduced metrics must ignore padding microbatches; summed metrics are unchanged.
+
+    [10, 5], [10, 5] pack into 2 real microbatches at 15 tokens; the fixture forces one
+    extra padding microbatch. Without the skip, policy_entropy = (1.0 + 1.0 + 0.0) / 3.
+    """
+    worker = PolicyWorkerBase.__new__(PolicyWorkerBase)
+    worker.cfg = SimpleNamespace(
+        micro_train_batch_size_per_gpu=1,
+        max_tokens_per_microbatch=MAX_TOKENS_PER_MICROBATCH,
+        algorithm=SimpleNamespace(policy_loss_type="regular"),
+    )
+    worker.strategy = MagicMock()
+    worker.strategy.all_reduce.side_effect = _identity_all_reduce
+    worker.device_mesh = MagicMock()
+
+    padding_flags = []
+
+    def fake_forward_backward_micro(experience, microbatch_weight, loss_fn=None, loss_fn_config=None):
+        is_padding = bool(experience.metadata and experience.metadata.get("is_padding_batch", False))
+        padding_flags.append(is_padding)
+        if is_padding:
+            # A fully-padding microbatch has an all-zero loss mask, so its masked-mean
+            # metrics come out as exactly 0, and its loss_fn_outputs are dummy entries
+            # (see TokenBasedBatchIterator._create_padding_microbatch).
+            return {"policy_entropy": 0.0, "policy_loss": 0.0, "loss_fn_outputs": [{"logprobs": [0.0]}]}
+        return {
+            "policy_entropy": 1.0,
+            "policy_loss": 0.5,
+            "loss_fn_outputs": [{"logprobs": [1.0]}, {"logprobs": [1.0]}],
+        }
+
+    worker._forward_backward_micro = fake_forward_backward_micro
+
+    out = worker.forward_backward(_make_batch([10, 10, 5, 5]))
+
+    # The padding microbatch still ran forward/backward (collective parity across DP ranks)...
+    assert len(padding_flags) == 3 and padding_flags.count(True) == 1
+    # ...but is excluded from mean-reduced metrics (2/3 without the skip).
+    assert out.metrics["policy_entropy"] == pytest.approx(1.0)
+    # Summed metrics are unaffected either way: padding contributes 0 to a sum.
+    assert out.metrics["policy_loss"] == pytest.approx(1.0)
+    # Diagnostics still count the padding microbatch.
+    assert out.metrics["num_microbatches"] == 3.0
+    assert out.metrics["num_padding_microbatches"] == 1.0
+    # loss_fn_outputs from the padding microbatch are excluded too: 2 real microbatches
+    # x 2 samples each remain, with no dummy [0.0] entry.
+    assert len(out.loss_fn_outputs) == 4
+    assert all(entry == {"logprobs": [1.0]} for entry in out.loss_fn_outputs)
+
+
+def test_critic_padding_microbatch_excluded_from_metrics(force_one_padding_microbatch):
+    """critic_loss is mean-reduced on the critic path (reduce_metrics without
+    sum_loss_metrics), so a padding microbatch's 0.0 would directly bias it:
+    (0.5 + 0.5 + 0.0) / 3 without the skip."""
+    worker = CriticWorkerBase.__new__(CriticWorkerBase)
+    worker.cfg = SimpleNamespace(
+        micro_train_batch_size_per_gpu=1,
+        max_tokens_per_microbatch=MAX_TOKENS_PER_MICROBATCH,
+    )
+    worker.strategy = MagicMock()
+    worker.strategy.all_reduce.side_effect = _identity_all_reduce
+
+    def fake_forward_backward_micro(experience, microbatch_weight=None):
+        if experience.metadata and experience.metadata.get("is_padding_batch", False):
+            return {"critic_loss": 0.0}
+        return {"critic_loss": 0.5}
+
+    worker._forward_backward_micro = fake_forward_backward_micro
+
+    out = worker.forward_backward(_make_batch([10, 10, 5, 5]))
+
+    assert out.metrics["critic_loss"] == pytest.approx(0.5)