diff --git a/tests/utils/debug/test_metrics.py b/tests/utils/debug/test_metrics.py
index 1b2f7f8faa1..2caf9fea123 100644
--- a/tests/utils/debug/test_metrics.py
+++ b/tests/utils/debug/test_metrics.py
@@ -16,7 +16,7 @@
 import torch
 
 from verl.protocol import DataProto
-from verl.utils.debug.metrics import calculate_debug_metrics
+from verl.utils.debug.metrics import _find_contiguous_segments, calculate_debug_metrics
 
 
 class TestMetrics(unittest.TestCase):
@@ -43,6 +43,95 @@ def test_calculate_debug_metrics(self):
         print(metrics)
         assert metrics["training/rollout_probs_diff_valid"] == 1
 
+    def test_find_contiguous_segments(self):
+        # Single segment
+        mask = torch.tensor([1, 1, 1, 0, 0])
+        assert _find_contiguous_segments(mask) == [(0, 3)]
+
+        # Multiple segments (multi-turn)
+        mask = torch.tensor([1, 1, 0, 0, 1, 1, 1, 0, 1])
+        assert _find_contiguous_segments(mask) == [(0, 2), (4, 7), (8, 9)]
+
+        # All zeros
+        mask = torch.tensor([0, 0, 0])
+        assert _find_contiguous_segments(mask) == []
+
+        # All ones
+        mask = torch.tensor([1, 1, 1])
+        assert _find_contiguous_segments(mask) == [(0, 3)]
+
+    def test_per_round_metrics_single_turn(self):
+        """Single contiguous response should produce 1 round."""
+        data = DataProto.from_dict(
+            {
+                "rollout_log_probs": torch.tensor([[-1.0, -2.0, -3.0, -4.0]]),
+                "old_log_probs": torch.tensor([[-1.1, -2.1, -3.1, -4.1]]),
+                "response_mask": torch.tensor([[1, 1, 1, 1]]),
+                "responses": torch.zeros((1, 4)),
+            }
+        )
+        metrics = calculate_debug_metrics(data)
+        assert metrics["per_round/total_rounds"] == 1
+        assert "per_round/round_0_abs_diff_mean" in metrics
+        self.assertAlmostEqual(metrics["per_round/round_0_abs_diff_mean"], 0.1, places=5)
+
+    def test_per_round_metrics_multi_turn(self):
+        """Multi-turn: two rounds separated by env tokens."""
+        # Round 0: positions 0-1, identical logprobs -> diff=0
+        # Round 1: positions 4-5, different logprobs -> diff=1.0
+        data = DataProto.from_dict(
+            {
+                "rollout_log_probs": torch.tensor([[-1.0, -2.0, -9.0, -9.0, -3.0, -4.0]]),
+                "old_log_probs": torch.tensor([[-1.0, -2.0, -9.0, -9.0, -4.0, -5.0]]),
+                "response_mask": torch.tensor([[1, 1, 0, 0, 1, 1]]),
+                "responses": torch.zeros((1, 6)),
+            }
+        )
+        metrics = calculate_debug_metrics(data)
+        assert metrics["per_round/total_rounds"] == 2
+        # Round 0: identical logprobs
+        self.assertAlmostEqual(metrics["per_round/round_0_abs_diff_mean"], 0.0, places=5)
+        # Round 1: diff of 1.0 each
+        self.assertAlmostEqual(metrics["per_round/round_1_abs_diff_mean"], 1.0, places=5)
+        # Max diff should be round 1
+        assert metrics["per_round/max_round_diff"] == 1
+        self.assertAlmostEqual(metrics["per_round/max_diff_value"], 1.0, places=5)
+
+    def test_per_round_metrics_batch(self):
+        """Batch with different number of rounds per sample."""
+        # Sample 0: 1 round (positions 0-2)
+        # Sample 1: 2 rounds (positions 0-1, positions 3-4)
+        data = DataProto.from_dict(
+            {
+                "rollout_log_probs": torch.tensor(
+                    [
+                        [-1.0, -2.0, -3.0, -9.0, -9.0],
+                        [-1.0, -2.0, -9.0, -3.0, -4.0],
+                    ]
+                ),
+                "old_log_probs": torch.tensor(
+                    [
+                        [-1.0, -2.0, -3.0, -9.0, -9.0],
+                        [-1.0, -2.0, -9.0, -3.5, -4.5],
+                    ]
+                ),
+                "response_mask": torch.tensor(
+                    [
+                        [1, 1, 1, 0, 0],
+                        [1, 1, 0, 1, 1],
+                    ]
+                ),
+                "responses": torch.zeros((2, 5)),
+            }
+        )
+        metrics = calculate_debug_metrics(data)
+        # Max rounds across batch is 2
+        assert metrics["per_round/total_rounds"] == 2
+        assert "per_round/round_0_abs_diff_mean" in metrics
+        assert "per_round/round_1_abs_diff_mean" in metrics
+        assert metrics["per_round/round_0_token_count"] == 5  # 3 from sample 0 + 2 from sample 1
+        assert metrics["per_round/round_1_token_count"] == 2  # only from sample 1
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/verl/utils/debug/metrics.py b/verl/utils/debug/metrics.py
index e7d57a2fec3..82ea3ce3404 100644
--- a/verl/utils/debug/metrics.py
+++ b/verl/utils/debug/metrics.py
@@ -60,6 +60,118 @@ def calculate_log_prob_diff(log_probs1: torch.Tensor, log_probs2: torch.Tensor,
     return torch.masked_select(full_diff, mask)
 
 
+def _find_contiguous_segments(mask_1d: torch.Tensor) -> list[tuple[int, int]]:
+    """Find contiguous segments of 1s in a 1D mask tensor.
+
+    Each contiguous segment of 1s represents one round of model generation
+    in a multi-turn trajectory. Segments are separated by 0s (environment
+    tokens like images, or padding).
+
+    Example:
+        mask = [1,1,1,1, 0,0,0,0,0, 1,1,1, 0,0,0,0,0, 1,1,1, 0,0,0]
+                |--R0--| |--env--| |--R1-| |--env--| |--R2-| |pad|
+        Returns: [(0, 4), (9, 12), (17, 20)]
+
+    Args:
+        mask_1d: 1D tensor with 0s and 1s
+
+    Returns:
+        List of (start, end) tuples for each contiguous segment of 1s.
+        end is exclusive (Python slice convention).
+    """
+    segments = []
+    in_segment = False
+    start = 0
+
+    for i in range(len(mask_1d)):
+        val = mask_1d[i].item() if isinstance(mask_1d[i], torch.Tensor) else mask_1d[i]
+        if val == 1 and not in_segment:
+            in_segment = True
+            start = i
+        elif val == 0 and in_segment:
+            in_segment = False
+            segments.append((start, i))
+
+    if in_segment:
+        segments.append((start, len(mask_1d)))
+
+    return segments
+
+
+def _calculate_per_round_metrics(
+    train_log_probs: torch.Tensor,
+    rollout_log_probs: torch.Tensor,
+    response_mask: torch.Tensor,
+) -> dict:
+    """Calculate per-round logprob mismatch metrics for multi-turn trajectories.
+
+    Identifies rounds by finding contiguous segments of 1s in response_mask,
+    then computes mean absolute logprob difference per round.
+
+    This is useful for multi-turn RL training where different rounds may have
+    different attention mask behavior (e.g., image window attention), causing
+    mismatch between training and rollout engines to vary across rounds.
+
+    Args:
+        train_log_probs: Log probs from training engine (batch_size, seq_len)
+        rollout_log_probs: Log probs from rollout engine (batch_size, seq_len)
+        response_mask: Mask for valid positions (batch_size, seq_len),
+            1=model generated token, 0=environment token or padding
+
+    Returns:
+        Dictionary with per-round metrics:
+            - per_round/total_rounds: Max number of rounds across batch
+            - per_round/round_{i}_abs_diff_mean: Mean |logprob_train - logprob_rollout| for round i
+            - per_round/round_{i}_token_count: Number of tokens in round i
+            - per_round/max_round_diff: Which round has the largest mean diff
+            - per_round/max_diff_value: The largest mean diff value
+    """
+    batch_size = train_log_probs.shape[0]
+
+    # round_idx -> list of (train_vals, rollout_vals)
+    all_round_data: dict[int, list[tuple[torch.Tensor, torch.Tensor]]] = {}
+    max_rounds = 0
+
+    for b in range(batch_size):
+        segments = _find_contiguous_segments(response_mask[b])
+        max_rounds = max(max_rounds, len(segments))
+
+        for round_idx, (start, end) in enumerate(segments):
+            if round_idx not in all_round_data:
+                all_round_data[round_idx] = []
+            all_round_data[round_idx].append((train_log_probs[b, start:end], rollout_log_probs[b, start:end]))
+
+    if not all_round_data:
+        return {"per_round/total_rounds": 0}
+
+    metrics: dict = {"per_round/total_rounds": max_rounds}
+    max_diff = -1.0
+    max_diff_round = -1
+
+    for round_idx in sorted(all_round_data.keys()):
+        train_all = torch.cat([t for t, _ in all_round_data[round_idx]])
+        rollout_all = torch.cat([r for _, r in all_round_data[round_idx]])
+
+        if train_all.numel() == 0:
+            continue
+
+        abs_diff = torch.abs(train_all - rollout_all)
+        mean_diff = abs_diff.mean().item()
+
+        metrics[f"per_round/round_{round_idx}_abs_diff_mean"] = mean_diff
+        metrics[f"per_round/round_{round_idx}_token_count"] = train_all.numel()
+
+        if mean_diff > max_diff:
+            max_diff = mean_diff
+            max_diff_round = round_idx
+
+    metrics["per_round/max_round_diff"] = max_diff_round
+    if max_diff_round >= 0:
+        metrics["per_round/max_diff_value"] = max_diff
+
+    return metrics
+
+
 def calculate_debug_metrics(data: DataProto) -> dict:
     """
     calculate rollout vs actor logprobs diff, for debugging purpose
@@ -100,10 +212,17 @@ def calculate_debug_metrics(data: DataProto) -> dict:
     response_mask_bool = response_mask.bool()
     pearson_corrcoef = pearson_correlation_coefficient(actor_probs, rollout_probs, response_mask_bool)
     rollout_probs_diff = calculate_log_prob_diff(actor_probs, rollout_probs, response_mask_bool)
-    return {
+
+    metrics = {
         "training/rollout_probs_diff_valid": 1,
         "training/rollout_probs_diff_max": torch.max(rollout_probs_diff).detach().item(),
         "training/rollout_probs_diff_mean": torch.mean(rollout_probs_diff).detach().item(),
         "training/rollout_probs_diff_std": torch.std(rollout_probs_diff).detach().item(),
         "training/rollout_actor_probs_pearson_corr": pearson_corrcoef,
     }
+
+    # Per-round logprob mismatch metrics for multi-turn trajectories
+    per_round_metrics = _calculate_per_round_metrics(actor_old_log_probs, rollout_old_log_probs, response_mask)
+    metrics.update(per_round_metrics)
+
+    return metrics