diff --git a/roll/distributed/strategy/megatron_strategy.py b/roll/distributed/strategy/megatron_strategy.py
index 4eeb4cc74..0c696ce94 100644
--- a/roll/distributed/strategy/megatron_strategy.py
+++ b/roll/distributed/strategy/megatron_strategy.py
@@ -341,10 +341,13 @@ def _pack_sequences(self, input_tensor, attention_mask, pad_packed_seq_to=None,
             seq_tokens = input_tensor_unpadded[b]
 
             # Pad sequence if needed
-            if padded_seq_len > seq_len:
-                seq_tokens = torch.nn.functional.pad(
-                    seq_tokens, (0, padded_seq_len - seq_len), value=pad_val
-                )
+            pad_seq_len = padded_seq_len - seq_len
+            if pad_seq_len > 0:
+                if seq_tokens.dim() == 1:
+                    pad_config = (0, pad_seq_len)
+                else:
+                    pad_config = tuple([0, 0] * (seq_tokens.dim() - 1) + [0, pad_seq_len])
+                seq_tokens = torch.nn.functional.pad(seq_tokens, pad_config, value=pad_val)
             all_input_tensor_padded.append(seq_tokens)
 
             if cp_size > 1:
@@ -411,6 +414,12 @@ def inner_forward_step(self, loss_func, data_iterator: Iterator[DataProto], mode
         attention_mask = data.batch["attention_mask"]
         labels = data.batch["labels"] if "labels" in data.batch else None  # labels is only used for sft
         packed_seq_params = None
+        position_ids = None
+        raw_position_ids = None
+        if "position_ids" in data.batch.keys() and data.batch["position_ids"].dim() == 3:  # qwen2vl/qwen3vl mrope
+            raw_position_ids = data.batch["position_ids"]
+            if raw_position_ids.size(1) == 4:
+                raw_position_ids = raw_position_ids[:, 1:, :].contiguous()  # (bsz, 4, seqlen) -> (bsz, 3, seqlen)
 
         if self.use_sequence_packing:
             input_ids, packed_seq_params, cu_seqlens, cu_seqlens_padded = self._pack_sequences(
@@ -418,13 +427,19 @@ def inner_forward_step(self, loss_func, data_iterator: Iterator[DataProto], mode
             )
             if labels is not None:
                 labels, _, _, _ = self._pack_sequences(labels, attention_mask, pad_val=IGNORE_INDEX)
+            if raw_position_ids is not None:
+                position_ids_for_pack = raw_position_ids.transpose(1, 2).contiguous()  # (bsz, C, seqlen) -> (bsz, seqlen, C)
+                packed_position_ids, _, _, _ = self._pack_sequences(position_ids_for_pack, attention_mask, pad_val=0)
+                position_ids = packed_position_ids.squeeze(0).transpose(0, 1).unsqueeze(1).contiguous()
             attention_mask = None
         else:
             input_ids = self._get_feature_on_this_cp_rank(input_ids, "input_ids")
             attention_mask = self._get_feature_on_this_cp_rank(attention_mask, "attention_mask")
             if labels is not None:
                 labels = self._get_feature_on_this_cp_rank(labels, "labels")
-        position_ids = None
+            if raw_position_ids is not None:
+                attention_mask = None
+                position_ids = raw_position_ids.transpose(0, 1)  # (bsz, C, seqlen) -> (C, bsz, seqlen)
         # attention_mask: SelfAttention defalt to te DotProductAttention with
         # AttnMaskType.causal in which attention_mask would not be used, pass
         # it mainly for moe aux loss without pad token and it is 2D