diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml
index 10a19c52c72d..d14b524b793a 100644
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -972,7 +972,6 @@ steps:
   - vllm/model_executor/layers/layernorm.py
   - vllm/model_executor/layers/activation.py
   - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - vllm/model_executor/layers/fused_moe/layer.py
   - tests/compile/test_fusion_attn.py
   - tests/compile/test_silu_mul_quant_fusion.py
   - tests/compile/distributed/test_fusion_all_reduce.py
diff --git a/tests/compile/distributed/test_fusions_e2e.py b/tests/compile/distributed/test_fusions_e2e.py
index 53c3f875d200..661172e1965b 100644
--- a/tests/compile/distributed/test_fusions_e2e.py
+++ b/tests/compile/distributed/test_fusions_e2e.py
@@ -111,17 +111,6 @@ class ModelBackendTestCase(NamedTuple):
                 async_tp=96,  # MLP is MoE, half the fusions of dense
             ),
         ),
-        ModelBackendTestCase(
-            model_name="openai/gpt-oss-20b",
-            model_kwargs=dict(max_model_len=1024, kv_cache_dtype="fp8"),
-            backend=AttentionBackendEnum.FLASHINFER,
-            matches=Matches(
-                attention_fusion=0,
-                allreduce_fusion=49,
-                sequence_parallel=49,
-                async_tp=48,
-            ),
-        ),
     ]
 
 elif current_platform.is_rocm():
diff --git a/vllm/distributed/device_communicators/symm_mem.py b/vllm/distributed/device_communicators/symm_mem.py
index 7a049b003cf7..eb1f173b1192 100644
--- a/vllm/distributed/device_communicators/symm_mem.py
+++ b/vllm/distributed/device_communicators/symm_mem.py
@@ -131,7 +131,7 @@ def all_reduce(
             return None
         if out is None:
             out = torch.empty_like(inp)
-        self.buffer[: inp.numel()].copy_(inp.reshape(-1))
+        self.buffer[: inp.numel()].copy_(inp.view(-1))
 
         # Determine which algorithm to use
         use_multimem = False
diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index bb30f1292a5f..0ef3130b2633 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -1690,10 +1690,6 @@ def forward_native(
             )
 
         def reduce_output(states: torch.Tensor) -> torch.Tensor:
-            # Slice before all_reduce to enable possible fusion
-            if self.hidden_size != og_hidden_states:
-                states = states[..., :og_hidden_states]
-
             if (
                 not self.is_sequence_parallel
                 and not self.use_dp_chunking
@@ -1716,12 +1712,11 @@ def reduce_output(states: torch.Tensor) -> torch.Tensor:
             if self.zero_expert_num is not None and self.zero_expert_num > 0:
                 assert isinstance(fused_output, tuple)
                 fused_output, zero_expert_result = fused_output
-                return (
-                    reduce_output(fused_output)
-                    + zero_expert_result[..., :og_hidden_states]
-                )
+                return (reduce_output(fused_output) + zero_expert_result)[
+                    ..., :og_hidden_states
+                ]
             else:
-                return reduce_output(fused_output)
+                return reduce_output(fused_output)[..., :og_hidden_states]
         else:
             if current_platform.is_tpu():
                 # TODO: Once the OOM issue for the TPU backend is resolved, we
@@ -1734,8 +1729,8 @@ def reduce_output(states: torch.Tensor) -> torch.Tensor:
                     hidden_states, router_logits, self.layer_name
                 )
             return (
-                reduce_output(shared_output),
-                reduce_output(fused_output),
+                reduce_output(shared_output)[..., :og_hidden_states],
+                reduce_output(fused_output)[..., :og_hidden_states],
             )
 
     def forward_cuda(