Merge tag 'v0.18.1rc0' into v0.18.1rc0-ori

docs/serving/expert_parallel_deployment.md

@@ -23,7 +23,6 @@ vLLM provides multiple communication backends for EP. Use `--all2all-backend` to
 | `deepep_low_latency` | Multi-node decode | CUDA graph support, masked layout, optimized for decode | Decode-dominated workloads, low-latency scenarios |
 | `flashinfer_nvlink_one_sided` | MNNVL systems | FlashInfer's one-sided A2A strategy for multi-node NVLink | High-throughput workloads |
 | `flashinfer_nvlink_two_sided` | MNNVL systems | FlashInfer's two-sided A2A strategy for multi-node NVLink | Systems with NVLink across nodes |
-| `naive` | Testing/debugging | Simple broadcast-based implementation | Debugging, not recommended for production |
 
 ## Single Node Deployment
 

docs/serving/offline_inference.md

@@ -16,7 +16,7 @@ After initializing the `LLM` instance, use the available APIs to perform model i
 The available APIs depend on the model type:
 
 - [Generative models](../models/generative_models.md) output logprobs which are sampled from to obtain the final output text.
-- [Pooling models](../models/pooling_models.md) output their hidden states directly.
+- [Pooling models](../models/pooling_models/README.md) output their hidden states directly.
 
 !!! info
     [API Reference](../api/README.md#offline-inference)