diff --git a/miles/utils/replay_base.py b/miles/utils/replay_base.py
index 100968a395..8e19b1ba67 100644
--- a/miles/utils/replay_base.py
+++ b/miles/utils/replay_base.py
@@ -95,10 +95,38 @@ def _get_replay_result(top_indices, scores, topk, *args, **kwargs):
 
             padding_mask = top_indices == -1
             if padding_mask.any():
-                top_indices[padding_mask] = (
-                    torch.arange(padding_mask.sum(), device=top_indices.device, dtype=top_indices.dtype)
-                    % scores.shape[1]
+                # Fill -1 padding slots with experts that are NOT already in the row's
+                # existing topk picks, ranked by score (most plausible filler first).
+                #
+                # The previous implementation `arange(N) % num_experts` could yield a
+                # filler equal to an expert id already present in the same row,
+                # producing within-row duplicates. Downstream code builds routing_map
+                # via scatter(top_indices, True), where duplicates collapse, so
+                # routing_map.sum() < num_tokens * topk. The MoE token dispatcher then
+                # computes input_splits from routing_map.sum() but uses
+                # num_out_tokens = num_tokens * topk for the permuted buffer size,
+                # so sum(input_splits) < input_tensor.shape[0] and all_to_all_single
+                # raises "RuntimeError: Split sizes doesn't match total dim 0 size".
+                # See https://github.com/radixark/miles/issues/1002.
+                n_experts = scores.shape[1]
+                non_pad = ~padding_mask
+                # Build [n_tokens, n_experts] used-mask via one_hot of non-pad picks.
+                # one_hot requires int64; top_indices is int32 per Sample.rollout_routed_experts.
+                used_mask = (
+                    torch.nn.functional.one_hot(top_indices.clamp(min=0).long(), num_classes=n_experts)
+                    .mul(non_pad.long().unsqueeze(-1))
+                    .sum(dim=1)
+                    .bool()
                 )
+                # Per-row score-ranked complement: used experts get -inf so they sink to the tail.
+                masked_scores = scores.masked_fill(used_mask, float("-inf"))
+                _, sorted_free = masked_scores.sort(dim=1, descending=True)
+                # The k-th -1 slot in each row gets sorted_free[row, k].
+                pad_cumsum = torch.cumsum(padding_mask.long(), dim=1) - 1
+                fill_values = torch.gather(sorted_free, 1, pad_cumsum.clamp(min=0)).to(
+                    top_indices.dtype
+                )
+                top_indices = torch.where(padding_mask, fill_values, top_indices)
 
             if return_probs:
                 return scores.gather(1, top_indices), top_indices