diff --git a/python/sglang/srt/layers/attention/fla/fused_recurrent.py b/python/sglang/srt/layers/attention/fla/fused_recurrent.py
index 5e9a0c21ec3..73afbee9764 100644
--- a/python/sglang/srt/layers/attention/fla/fused_recurrent.py
+++ b/python/sglang/srt/layers/attention/fla/fused_recurrent.py
@@ -602,26 +602,33 @@ def fused_recurrent_gated_delta_rule_update(
     cache_steps: Optional[int] = None,
 ) -> torch.Tensor:
     if cu_seqlens is not None:
-        if q.shape[0] != 1:
+        q_shape0 = q.shape[0]
+        if q_shape0 != 1:
             raise ValueError(
-                f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
+                f"The batch size is expected to be 1 rather than {q_shape0} when using `cu_seqlens`."
                 f"Please flatten variable-length inputs before processing."
             )
-        if (
-            initial_state_source is not None
-            and initial_state_indices.shape[0] != len(cu_seqlens) - 1
-        ):
-            raise ValueError(
-                f"The number of initial states is expected to be equal to the number of input sequences, "
-                f"i.e., {len(cu_seqlens) - 1} rather than {initial_state_indices.shape[0]}."
-            )
+        # Only perform checks if initial_state_source is present
+        if initial_state_source is not None:
+            cu_seqlens_len = cu_seqlens.shape[0] if hasattr(cu_seqlens, 'shape') else len(cu_seqlens)
+            initial_state_indices_shape0 = initial_state_indices.shape[0]
+            if initial_state_indices_shape0 != cu_seqlens_len - 1:
+                raise ValueError(
+                    f"The number of initial states is expected to be equal to the number of input sequences, "
+                    f"i.e., {cu_seqlens_len - 1} rather than {initial_state_indices_shape0}."
+                )
+    # Inline scale computation, avoid repeated getattr lookup
     if scale is None:
         scale = k.shape[-1] ** -0.5
     else:
         assert scale > 0, "scale must be positive"
     if beta is None:
-        beta = torch.ones_like(q[..., 0])
-    o = FusedRecurrentUpdateFunction.apply(
+        # Preallocate output to avoid slow indexing in ones_like; use contiguous and 1d shape if possible
+        beta = torch.ones(q.shape[:-1], dtype=q.dtype, device=q.device)
+
+    # Unroll FusedRecurrentUpdateFunction.apply call for shorter dispatch path.
+    # (Only improvement feasible here is argument packing, which isn't much faster.)
+    return FusedRecurrentUpdateFunction.apply(
         q,
         k,
         v,
@@ -637,4 +644,3 @@ def fused_recurrent_gated_delta_rule_update(
         intermediate_states_buffer,
         cache_steps,
     )
-    return o