[Doc] Address README review feedback

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>

README.md

@@ -16,14 +16,10 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
+- [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
-- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
+- [2026/06] Starting with [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0), vLLM-Omni publishes a stable release aligned with every even-numbered upstream vLLM minor version. [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0), [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0), [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0), and [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) continued this cadence, expanding omni and world-model support with [NVIDIA Cosmos3](recipes/cosmos3/Cosmos3-Nano.md) and DreamZero, adding models such as MiniCPM-o 4.5, MOSS-TTS, and Lance, and advancing TTS, diffusion, distributed execution, quantization, RL integration through [VeRL-Omni](https://github.com/verl-project/verl-omni), and CUDA/ROCm/MUSA/NPU/XPU coverage.
-- [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
-- [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
 - [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup!
-- [2026/03] **[vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills)** is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like **Cursor IDE**, **Claude**, **Codex**, and more.
-- [2026/02] We released [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0) - A major alignment + capability release that rebases onto **upstream vLLM v0.16.0** and significantly expands performance, distributed execution, and production readiness across **Qwen3-Omni / Qwen3-TTS**, **Bagel**, **MiMo-Audio**, **GLM-Image** and the **Diffusion (DiT) image/video stack**—while also improving platform coverage (CUDA / ROCm / NPU / XPU), CI quality, and documentation.
-- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest [paper](https://arxiv.org/abs/2602.02204) for architecture design and performance results.
 - [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.
 
 ---
@@ -55,12 +51,13 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 ## Getting Started

docs/README.md

@@ -52,12 +52,13 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 For more information, checkout the following:

docs/cli/README.md

@@ -18,18 +18,12 @@ Specify the port:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-For a migrated model, load a custom deploy configuration with `--deploy-config`:
+Load a custom deploy configuration with `--deploy-config`:
 
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --deploy-config /path/to/deploy_config.yaml
 ```
 
-The deprecated `--stage-configs-path` flag is retained for models that still use the legacy `stage_args` schema:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --stage-configs-path /path/to/legacy_stage_config.yaml
-```
-
 ## bench
 
 Run benchmark tests for online serving throughput.

docs/cli/serve.md

@@ -8,7 +8,6 @@ The stage-based CLI is designed for deployments that require launching each pipe
 - For **migrated models** that utilize the bundled deployment YAML configurations located in
   `vllm_omni/deploy/`, the `--deploy-config` flag is only required to override the default configuration. By default, executing `vllm serve MODEL --omni ...`
   automatically loads the bundled deployment configuration.
-- For custom legacy `stage_args` YAMLs, pass the file with `--stage-configs-path`.
 
 Example: Initializing Stage 0 (Orchestrator and API Server):
 The commands below show a common device mapping where Stage 0 uses GPU 0 and
@@ -32,7 +31,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
     --omni-master-port 26000
 ```
 
-When utilizing a custom deployment YAML based on the new schema, append `--deploy-config /path/to/override.yaml` to each command execution. Conversely, for legacy models, substitute this parameter with `--stage-configs-path /path/to/stage_configs.yaml`.
+When utilizing a custom deployment YAML, append `--deploy-config /path/to/override.yaml` to each command execution.
 
 In the standard execution paradigm, the `--stage-overrides` argument is utilized to apply stage-specific configurations from a single CLI command.
 However, under the **stage-based CLI** paradigm, where each process strictly encapsulates a single stage, it is recommended to specify tuning parameters directly via discrete command-line flags for the respective stage, rather than constructing a composite `--stage-overrides` JSON string.

docs/configuration/README.md

@@ -2,7 +2,7 @@
 
 This section lists the most common options for running vLLM-Omni.
 
-For options within a vLLM Engine. Please refer to [vLLM Configuration](https://docs.vllm.ai/en/v0.16.0/configuration/index.html)
+For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).
 
 Currently, the main options are maintained by stage configs for each model.
 

docs/configuration/composable_parallel.md

@@ -34,12 +34,8 @@ Two additional `StrategySpec` slots — `layer_hook_specs` and `kernel_specs`
 | Flag | Description |
 |------|-------------|
 | `--strategy-config PATH` | Path to a `strategy.yaml`. Only takes effect on registry-based deploy paths (`--deploy-config` or the bundled default deploy YAML). Its derived sizing is overlaid onto the merged stages *before* CLI overrides. |
-| `--stage-configs-path PATH` | **Legacy.** Loads the old `stage_args` YAML schema for un-migrated models. Passing `--strategy-config` together with this path is silently inapplicable — vLLM-Omni emits a warning and the strategy is ignored (see `vllm_omni/entrypoints/utils.py`, lines 372–380). |
 | `--omni-lb-policy POLICY` | Orchestrator-wide load-balancer policy. Strategy-derived from any `stage_replica` axis; an explicit CLI value wins and must match the derived one or `AsyncOmniEngine` raises `Conflicting load-balancer policy` at construction. |
 
-!!! warning
-    `--strategy-config` cannot be used with `--stage-configs-path` (the legacy deploy path). If your model has not been migrated to the registry-based pipeline yet, the strategy is silently dropped with a warning. Migrate to a registry-based model or use `--deploy-config` to apply a strategy.
-
 ## Sub-schemas
 
 ### `axis`
@@ -216,17 +212,6 @@ Strategy keys MUST be `model_stage` *names* (strings), the same labels stage con
 !!! warning "Mixing CLI overrides with a strategy on the same axis"
     A strategy is meant to be the single writer for the axes it declares. If a CLI override (`--tensor-parallel-size 2`, `--stage-overrides '{"0": {"tensor_parallel_size": 2}}'`) sets the same axis, the **CLI value wins** and a warning is emitted from `_reconcile_strategy_with_cli`. The device-layout guard then re-runs against the effective layout, so a CLI override that breaks `tp * dp * pp * num_replicas` will still fail fast at config time.
 
-!!! warning "Passing `--strategy-config` against the legacy `--stage-configs-path`"
-    Composable parallel only applies on the registry-based deploy path. If a model still resolves through the legacy `stage_args` YAML (loaded via `--stage-configs-path`), `--strategy-config` is silently inapplicable and emits:
-
-    ```text
-    --strategy-config (<path>) was provided but model '<model>' resolves via the
-    legacy stage_configs YAML path, which does not support composable-parallel
-    strategies; the strategy is ignored. Use a registry-based model to apply it.
-    ```
-
-    Migrate the model to the new deploy schema (see `configuration/stage_configs.md`) before applying a strategy.
-
 !!! warning "Conflicting `omni_lb_policy` across stages"
     `omni_lb_policy` is a single pipeline-wide knob. If two `stage_replica` axes in different stages derive different policies, `apply_strategy_specs` raises:
 

docs/configuration/pd_disaggregation.md

@@ -158,7 +158,7 @@ runtime:
 
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --stage-configs-path /path/to/qwen3_omni_pd.yaml
+    --deploy-config /path/to/qwen3_omni_pd.yaml
 ```
 
 ## Operational Notes

docs/configuration/stage_configs.md

@@ -1,9 +1,9 @@
-# Deploy and legacy stage configurations
+# Pipeline and deploy configurations
 
-In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run. Models that have not migrated to this split still use legacy stage configurations with a `stage_args` schema.
+In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run.
 
 !!! note
-    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when neither `--stage-configs-path` nor `--deploy-config` is provided — the model registry resolves the right pipeline + deploy YAML by `model_type`. Custom legacy `stage_args` YAMLs are still accepted via `--stage-configs-path`.
+    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when `--deploy-config` is omitted. The model registry resolves the right pipeline and deploy YAML by `model_type`.
 
 ## Deploy configuration schema
 
@@ -86,10 +86,9 @@ stages:
 
 | Flag | Description |
 |------|-------------|
-| `--deploy-config PATH` | Load a new-schema deploy YAML. It is mutually exclusive with `--stage-configs-path`. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
+| `--deploy-config PATH` | Load a deploy YAML. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
 | `--stage-overrides JSON` | Per-stage JSON overrides, e.g. `'{"0":{"gpu_memory_utilization":0.5}}'`. Per-stage values always win over global flags. |
 | `--async-chunk` / `--no-async-chunk` | Flip the deploy YAML's `async_chunk:` bool. Unset (default) leaves the YAML value in force. |
-| `--stage-configs-path` | **Deprecated.** Load a legacy `stage_args` YAML. New-schema YAMLs passed to this flag are auto-detected for compatibility, but should use `--deploy-config`. It is mutually exclusive with `--deploy-config` and will be removed in a future release. |
 
 ### Stage-Based CLI Paradigm
 
@@ -119,8 +118,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
     --omni-master-port 26000
 ```
 
-When instantiating a custom deployment YAML conforming to the updated schema, append the `--deploy-config /path/to/override.yaml` directive
+When instantiating a custom deployment YAML, append the `--deploy-config /path/to/override.yaml` directive to all node invocations.
-to all node invocations. For legacy architectures (e.g., BAGEL) configured via deprecated `stage_args:` schemas, continue to specify the relevant configuration via `--stage-configs-path /path/to/config.yaml`.
 
 In the context of standard initialization architectures, utilizing the `--stage-overrides` parameter operates as the optimal methodology
 for delineating stage-specific tuning from the CLI interface:
@@ -220,8 +218,7 @@ Therefore, as a core part of vLLM-Omni, a model's pipeline and deployment config
 
 To override specific parameters, explicitly inject the customized configuration schema
 in both online and offline instantiation flows. Use the `--deploy-config` flag
-when loading a deploy configuration, reserving the `--stage-configs-path` parameter
+when loading a deploy configuration.
-exclusively to maintain compatibility with legacy `stage_args` YAML constructs.
 
 Examples:
 
@@ -236,11 +233,6 @@ For online serving:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
-Legacy online serving:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
-```
 !!! important
     We are actively iterating on the definition of deployment configurations, and we welcome feedback from users and developers.
 

docs/contributing/model/adding_diffusion_model.md

@@ -653,7 +653,7 @@ python end2end.py --model your-org/your-model-name --modality text2img --prompts
 Mirror BAGEL’s online serving setup:
 
 - Server launcher: `examples/online_serving/your_model_name/run_server.sh`
-  - Wrap `vllm serve ... --omni --port ...` (and `--stage-configs-path ...` if needed)
+  - Wrap `vllm serve ... --omni --port ...` (and `--deploy-config ...` if needed)
 - Client: `examples/online_serving/your_model_name/openai_chat_client.py`
   - Send requests to `POST /v1/chat/completions`
   - Support multimodal inputs (e.g., base64 image) if your model needs it

docs/contributing/model/adding_omni_model.md

@@ -48,8 +48,8 @@ vllm_omni/deploy/
 ```
 
 New in-tree models should register their `PipelineConfig` in
-`vllm_omni/config/pipeline_registry.py`; use `--stage-configs-path` only for
+`vllm_omni/config/pipeline_registry.py`; use `--deploy-config` for custom
-custom legacy `stage_args` YAMLs.
+deployment YAMLs.
 
 ## Step-by-Step Implementation
 

docs/contributing/model/adding_tts_model.md

@@ -449,9 +449,8 @@ Key configuration fields:
 !!! note
     New in-tree models should define frozen topology in `models/<model>/pipeline.py`,
     register it in `vllm_omni/config/pipeline_registry.py`, and put deployment
-    knobs in `vllm_omni/deploy/<model>.yaml`. Legacy `stage_args` YAMLs are still
+    knobs in `vllm_omni/deploy/<model>.yaml`. Use `--deploy-config` to load a
-    accepted for custom configs via `--stage-configs-path`, but should not be used
+    custom deployment YAML.
-    for new bundled defaults.
 
 ### Batch mode
 

docs/design/qwen3_omni_tts_performance_optimization.md

@@ -419,8 +419,7 @@ Notes:
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \
   --omni \
-  --port 8091 \
+  --port 8091
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
 ```
 
 #### 3) Key config knobs
@@ -494,7 +493,7 @@ The default config (`qwen3_tts.yaml`) enables the full optimization stack:
 vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
   --omni \
   --port 8000 \
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts_no_async_chunk.yaml
+  --no-async-chunk
 ```
 
 #### 3) Key config knobs

docs/getting_started/installation/README.md

@@ -1,5 +1,8 @@
 # Installation
 
+!!! important
+    vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the stable v0.26.0 instructions pin vLLM 0.26.0.
+
 vLLM-Omni supports the following hardware platforms:
 
 - [GPU](gpu.md)

docs/getting_started/installation/gpu/cuda.inc.md

@@ -5,7 +5,7 @@
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]
 
-vLLM-Omni depends vLLM. So please follow instructions below mainly for vLLM.
+vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.26.x release line uses vLLM 0.26.x.
 
 !!! note
     PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
@@ -124,7 +124,7 @@ If you want to specify the base vLLM version:
 ```bash
 DOCKER_BUILDKIT=1 docker build \
   -f docker/Dockerfile.cuda \
-  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.22.1 \
+  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.26.0 \
   -t vllm-omni-cuda .
 ```
 

docs/getting_started/installation/npu/npu.inc.md

@@ -53,16 +53,20 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886
 You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.)
 
 ```bash
-# Pin vLLM version to 0.18.0
+# Pin vLLM and vLLM-Ascend to the v0.26 release line
-git clone -b v0.18.0 https://github.com/vllm-project/vllm.git
+git clone -b v0.26.0 https://github.com/vllm-project/vllm.git
+cd vllm
 VLLM_TARGET_DEVICE=empty pip install -v -e .
+cd ..
 
-git clone -b v0.18.0rc1 https://github.com/vllm-project/vllm-ascend.git
+git clone -b releases/v0.26.0rc https://github.com/vllm-project/vllm-ascend.git
+cd vllm-ascend
 pip install -v -e .
+cd ..
 
 # Install vLLM-Omni from the latest main branch
 git clone https://github.com/vllm-project/vllm-omni.git
-cd /vllm-workspace/vllm-omni
+cd vllm-omni
 pip install -v -e . --no-build-isolation
 # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
 export VLLM_WORKER_MULTIPROC_METHOD=spawn

docs/user_guide/examples/online_serving/bagel.md

@@ -40,7 +40,7 @@ bash run_server_stage_cli.sh --stage 0
 bash run_server_stage_cli.sh --stage 1
 ```
 
-To use a custom deploy YAML (note: `--stage-configs-path` is deprecated in favor of `--deploy-config`):
+To use a custom deploy YAML:
 
 ```bash
 vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 \

docs/user_guide/examples/online_serving/glm_image.md

@@ -11,8 +11,8 @@ memory, sampling params) live in `vllm_omni/deploy/glm_image.yaml`.
 vllm serve zai-org/GLM-Image --omni --port 8091
 ```
 
-The config system auto-detects the pipeline from the model's `model_index.json` — no
+The config system auto-detects the pipeline from the model's `model_index.json`, so
-manual `--stage-configs-path` or `--deploy-config` needed.
+no manual `--deploy-config` is needed.
 
 By default, stage 0 (AR) runs on GPU 0 and stage 1 (Diffusion) on GPU 1. To colocate
 both stages on a single GPU, override per stage:

docs/user_guide/examples/online_serving/qwen2_5_omni.md

@@ -15,9 +15,9 @@ Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file, launch the server with command below
+To use a custom deploy config, launch the server with the command below:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 ### Send Multi-modal Request
@@ -182,7 +182,7 @@ The script supports the following arguments:
 - `--model`: Model name/path (default: Qwen/Qwen2.5-Omni-7B)
 - `--server-port`: Port for vLLM server (default: 8091)
 - `--gradio-port`: Port for Gradio demo (default: 7861)
-- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--deploy-config`: Path to a custom deploy config YAML file (optional)
 - `--server-host`: Host for vLLM server (default: 0.0.0.0)
 - `--gradio-ip`: IP for Gradio demo (default: 127.0.0.1)
 - `--share`: Share Gradio demo publicly (creates a public link)
@@ -195,9 +195,9 @@ The script supports the following arguments:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file:
+To use a custom deploy config:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 **Step 2: Run the Gradio demo**

docs/user_guide/fault_injection_reliability_matrix.md

@@ -69,19 +69,19 @@ The behaviors and conclusions above are summarized from current fault injection
 
 ### Example: `SIGTERM` fault injection log (Qwen3-Omni)
 
-The following full log is from a real run where a `SIGTERM` signal was sent to the serve root process.
+The following log is based on a real run where a `SIGTERM` signal was sent to the serve root process. Its command line has been normalized to the current `--deploy-config` option.
 
 ```text
-Launching OmniServer with: /workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+Launching OmniServer with: /workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 Server ready on 127.0.0.1:60675 (OmniServer startup took 206.023s)
 OmniServer started successfully
-[reliability][process-kill] current_server_proc pid=556855 name=python3 cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[reliability][process-kill] current_server_proc pid=556855 name=python3 cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 [reliability][process-kill] current_server_proc pid=557118 name=python3 cmdline=/workspace/.venv/bin/python3 -c from multiprocessing.resource_tracker import main;main(57)
 [reliability][process-kill] current_server_proc pid=557119 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 [reliability][process-kill] current_server_proc pid=557122 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 [reliability][process-kill] current_server_proc pid=558201 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 
-[reliability][process-kill] root-kill pid=556855 name=python3 signal=SIGTERM cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[reliability][process-kill] root-kill pid=556855 name=python3 signal=SIGTERM cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [omni_base.py:463] [AsyncOmni] Shutting down
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [async_omni_engine.py:2397] [AsyncOmniEngine] Shutting down Orchestrator
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [orchestrator.py:351] [Orchestrator] Received shutdown signal

examples/online_serving/audex/run_server.sh

@@ -53,5 +53,5 @@ vllm-omni serve "$MODEL" \
     --host 0.0.0.0 \
     --port "$PORT" \
     --trust-remote-code \
-    --stage-configs-path "$DEPLOY_YAML" \
+    --deploy-config "$DEPLOY_YAML" \
     --omni

examples/online_serving/chart-helm/templates/_helpers.tpl

@@ -28,8 +28,11 @@ If image.command is set, uses that as a full override.
 - {{ .Values.omniArgs.numGpus | quote }}
 {{- end }}
 {{- if .Values.omniArgs.stageConfigsPath }}
-- "--stage-configs-path"
+{{- fail "omniArgs.stageConfigsPath has been removed; use omniArgs.deployConfigPath" }}
-- {{ .Values.omniArgs.stageConfigsPath | quote }}
+{{- end }}
+{{- if .Values.omniArgs.deployConfigPath }}
+- "--deploy-config"
+- {{ .Values.omniArgs.deployConfigPath | quote }}
 {{- end }}
 {{- if and .Values.omniArgs.cacheBackend (ne .Values.omniArgs.cacheBackend "none") }}
 - "--cache-backend"

examples/online_serving/chart-helm/tests/deployment_test.yaml

@@ -78,6 +78,26 @@ tests:
           path: spec.template.spec.containers[0].command
           content: "--enable-layerwise-offload"
 
+  - it: should include deploy config in command
+    set:
+      omniArgs:
+        deployConfigPath: "/configs/deploy.yaml"
+    asserts:
+      - equal:
+          path: spec.template.spec.containers[0].command[8]
+          value: "--deploy-config"
+      - equal:
+          path: spec.template.spec.containers[0].command[9]
+          value: "/configs/deploy.yaml"
+
+  - it: should reject removed stage config value
+    set:
+      omniArgs:
+        stageConfigsPath: "/configs/legacy.yaml"
+    asserts:
+      - failedTemplate:
+          errorMessage: "omniArgs.stageConfigsPath has been removed; use omniArgs.deployConfigPath"
+
   - it: should use full command override when image.command is set
     set:
       image:

examples/online_serving/chart-helm/values.yaml

@@ -67,8 +67,8 @@ omniArgs:
   enableCpuOffload: false
   # -- Number of GPUs for diffusion inference (empty = auto)
   numGpus:
-  # -- Path to stage configs file (empty = auto-detected from model)
+  # -- Path to deploy config file (empty = auto-detected from model)
-  stageConfigsPath:
+  deployConfigPath:
   # -- Cache backend: none, tea_cache, or cache_dit
   cacheBackend: "none"
   # -- Default sampling params as a JSON string

examples/online_serving/minicpmo/run_gradio_demo.sh

@@ -3,7 +3,7 @@
 #
 # Prereq:
 #   Start a vllm-omni OpenAI server for MiniCPM-o 4.5 on :8099 (see the
-#   8x4090 stage config under vllm_omni/model_executor/stage_configs).
+#   8x4090 deploy config at vllm_omni/deploy/minicpmo_4_5_8x4090.yaml).
 set -e
 
 HERE="$(cd "$(dirname "$0")" && pwd)"

examples/online_serving/qwen2_5_omni/README.md

@@ -12,9 +12,9 @@ Please refer to [README.md](../../../README.md)
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file, launch the server with command below
+To use a custom deploy config, launch the server with the command below:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 ### Send Multi-modal Request
@@ -173,7 +173,7 @@ The script supports the following arguments:
 - `--model`: Model name/path (default: Qwen/Qwen2.5-Omni-7B)
 - `--server-port`: Port for vLLM server (default: 8091)
 - `--gradio-port`: Port for Gradio demo (default: 7861)
-- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--deploy-config`: Path to a custom deploy config YAML file (optional)
 - `--server-host`: Host for vLLM server (default: 0.0.0.0)
 - `--gradio-ip`: IP for Gradio demo (default: 127.0.0.1)
 - `--share`: Share Gradio demo publicly (creates a public link)
@@ -186,9 +186,9 @@ The script supports the following arguments:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file:
+To use a custom deploy config:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 **Step 2: Run the Gradio demo**

examples/online_serving/qwen2_5_omni/run_gradio_demo.sh

@@ -13,7 +13,7 @@ set -e
 MODEL="Qwen/Qwen2.5-Omni-7B"
 SERVER_PORT=8091
 GRADIO_PORT=7861
-STAGE_CONFIGS_PATH=""
+DEPLOY_CONFIG_PATH=""
 SERVER_HOST="0.0.0.0"
 GRADIO_IP="127.0.0.1"
 GRADIO_SHARE=false
@@ -33,8 +33,8 @@ while [[ $# -gt 0 ]]; do
             GRADIO_PORT="$2"
             shift 2
             ;;
-        --stage-configs-path)
+        --deploy-config)
-            STAGE_CONFIGS_PATH="$2"
+            DEPLOY_CONFIG_PATH="$2"
             shift 2
             ;;
         --server-host)
@@ -56,7 +56,7 @@ while [[ $# -gt 0 ]]; do
             echo "  --model MODEL                 Model name/path (default: Qwen/Qwen2.5-Omni-7B)"
             echo "  --server-port PORT            Port for vLLM server (default: 8091)"
             echo "  --gradio-port PORT            Port for Gradio demo (default: 7861)"
-            echo "  --stage-configs-path PATH     Path to custom stage configs YAML file (optional)"
+            echo "  --deploy-config PATH          Path to custom deploy config YAML file (optional)"
             echo "  --server-host HOST            Host for vLLM server (default: 0.0.0.0)"
             echo "  --gradio-ip IP                IP for Gradio demo (default: 127.0.0.1)"
             echo "  --share                       Share Gradio demo publicly"
@@ -87,8 +87,8 @@ echo "=========================================="
 
 # Build vLLM server command
 SERVER_CMD=("vllm" "serve" "$MODEL" "--omni" "--port" "$SERVER_PORT" "--host" "$SERVER_HOST")
-if [ -n "$STAGE_CONFIGS_PATH" ]; then
+if [ -n "$DEPLOY_CONFIG_PATH" ]; then
-    SERVER_CMD+=("--stage-configs-path" "$STAGE_CONFIGS_PATH")
+    SERVER_CMD+=("--deploy-config" "$DEPLOY_CONFIG_PATH")
 fi
 
 # Function to cleanup on exit

examples/online_serving/text_to_speech/README.md

@@ -336,7 +336,7 @@ vllm serve OpenMOSS-Team/MOSS-TTS-Nano --omni --port 8091
 # or:
 ./moss_tts_nano/run_server.sh
 ```
-The deploy config at `vllm_omni/deploy/moss_tts_nano.yaml` auto-loads; no `--stage-configs-path`, `--trust-remote-code`, or `--enforce-eager` flags are needed.
+The deploy config at `vllm_omni/deploy/moss_tts_nano.yaml` auto-loads; no `--deploy-config`, `--trust-remote-code`, or `--enforce-eager` flags are needed.
 
 ### Sending requests
 ```bash

tests/dfx/reliability/test_reliability_qwen3_omni.py

@@ -241,12 +241,12 @@ def _fault_keywords_match_response(*, status: int, body: bytes) -> bool:
     return any(key in text for key in body_fault_hints)
 
 
-def _stage_config_path_from_omni_server(omni_server: _HasServeArgs) -> str | None:
+def _deploy_config_path_from_omni_server(omni_server: _HasServeArgs) -> str | None:
     args: list[str] = omni_server.serve_args
     for i, arg in enumerate(args):
-        if arg == "--stage-configs-path" and i + 1 < len(args):
+        if arg == "--deploy-config" and i + 1 < len(args):
             return args[i + 1]
-        if arg.startswith("--stage-configs-path="):
+        if arg.startswith("--deploy-config="):
             return arg.split("=", 1)[1]
     return None
 
@@ -331,7 +331,7 @@ def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
     """
     device_spec = resolve_oom_device_spec(
         OOM_INJECTION_CONFIG,
-        _stage_config_path_from_omni_server(omni_server_function),
+        _deploy_config_path_from_omni_server(omni_server_function),
     )
     handle = inject_gpu_oom(
         device=device_spec,
@@ -394,7 +394,7 @@ def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
 def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_function, openai_client_function) -> None:
     device_spec = resolve_oom_device_spec(
         OOM_INJECTION_CONFIG,
-        _stage_config_path_from_omni_server(omni_server_function),
+        _deploy_config_path_from_omni_server(omni_server_function),
     )
     handle = inject_gpu_oom(
         device=device_spec,
@@ -437,7 +437,7 @@ def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_functi
 def test_reliability_fault_gpu_oom_concurrent_pressure_failure(omni_server_function, openai_client_function) -> None:
     device_spec = resolve_oom_device_spec(
         OOM_INJECTION_CONFIG,
-        _stage_config_path_from_omni_server(omni_server_function),
+        _deploy_config_path_from_omni_server(omni_server_function),
     )
     handle = inject_gpu_oom(
         device=device_spec,
@@ -791,7 +791,7 @@ def test_reliability_fault_gpu_oom_state_converges_after_fault_removed(
     """
     device_spec = resolve_oom_device_spec(
         OOM_RECOVER_INJECTION_CONFIG,
-        _stage_config_path_from_omni_server(omni_server_function),
+        _deploy_config_path_from_omni_server(omni_server_function),
     )
     handle = inject_gpu_oom(
         device=device_spec,

tests/e2e/accuracy/helpers.py

@@ -696,7 +696,7 @@ def _dreamzero_omni_server_from_params(params):
 
     server_args = list(params.server_args or [])
     if params.stage_config_path is not None:
-        server_args += ["--stage-configs-path", params.stage_config_path]
+        server_args += ["--deploy-config", params.stage_config_path]
     return OmniServer(
         params.model,
         server_args,

tests/e2e/accuracy/test_hunyuan_image3.py

@@ -359,12 +359,12 @@ def _run_offline(deploy_config_path: str, output_path: Path) -> tuple[Image.Imag
     return image, cot_text, elapsed
 
 
-def _run_online(stage_configs_path: str, output_path: Path) -> tuple[Image.Image, str, float]:
+def _run_online(deploy_config_path: str, output_path: Path) -> tuple[Image.Image, str, float]:
     from benchmarks.accuracy.common import decode_base64_image, pil_to_png_bytes
 
     server_args = [
-        "--stage-configs-path",
+        "--deploy-config",
-        stage_configs_path,
+        deploy_config_path,
         "--stage-init-timeout",
         "300",
         "--init-timeout",

tests/entrypoints/test_serve.py

@@ -46,6 +46,26 @@ def test_serve_parser_accepts_strategy_config() -> None:
     assert args.get_explicit_kwargs_dict()["strategy_config"] == "/tmp/strategy.yaml"
 
 
+def test_serve_parser_accepts_deploy_config() -> None:
+    parser = TrackingArgumentParser()
+    subparsers = parser.add_subparsers(dest="subcommand")
+    OmniServeCommand().subparser_init(subparsers)
+
+    args = parser.parse_args(["serve", "fake-model", "--omni", "--deploy-config", "/tmp/deploy.yaml"])
+
+    assert args.deploy_config == "/tmp/deploy.yaml"
+    assert args.get_explicit_kwargs_dict()["deploy_config"] == "/tmp/deploy.yaml"
+
+
+def test_serve_parser_rejects_stage_configs_path() -> None:
+    parser = TrackingArgumentParser()
+    subparsers = parser.add_subparsers(dest="subcommand")
+    OmniServeCommand().subparser_init(subparsers)
+
+    with pytest.raises(SystemExit):
+        parser.parse_args(["serve", "fake-model", "--omni", "--stage-configs-path", "/tmp/stages.yaml"])
+
+
 def test_serve_parser_accepts_four_way_cfg_parallelism() -> None:
     parser = TrackingArgumentParser()
     subparsers = parser.add_subparsers(dest="subcommand")
@@ -66,7 +86,6 @@ def _make_headless_args(**kwargs) -> TrackingNamespace:
         "omni_replica_address": None,
         "omni_dp_size_local": 1,
         "worker_backend": "multi_process",
-        "stage_configs_path": None,
         "deploy_config": None,
         "log_stats": False,
         "disable_log_stats": False,
@@ -154,6 +173,7 @@ def test_run_headless_parses_and_forwards_stage_overrides(mocker: MockerFixture)
     captured: dict = {}
 
     def _fake_resolve(*args, **kwargs):
+        captured["args"] = args
         captured.update(kwargs)
         # Return a stage that does NOT match stage_id=0 so run_headless stops
         # right after the resolver call (we only care about how it was called).
@@ -166,12 +186,15 @@ def test_run_headless_parses_and_forwards_stage_overrides(mocker: MockerFixture)
 
     args = _make_headless_args(
         stage_id=0,
+        deploy_config="/tmp/deploy.yaml",
         strategy_config="/tmp/strategy.yaml",
         stage_overrides='{"0": {"devices": "0,1"}, "1": {"devices": "2"}}',
     )
     with pytest.raises(ValueError, match="No stage config found for stage_id=0"):
         run_headless(args)
 
+    assert captured["args"][1] is None
+    assert captured["deploy_config_path"] == "/tmp/deploy.yaml"
     assert captured["stage_overrides"] == {"0": {"devices": "0,1"}, "1": {"devices": "2"}}
     assert captured["strategy_config_path"] == "/tmp/strategy.yaml"
 

tests/helpers/runtime.py

@@ -598,7 +598,7 @@ class OmniServerStageCli(OmniServer):
             "serve",
             self.model,
             "--omni",
-            "--stage-configs-path",
+            "--deploy-config",
             self.stage_config_path,
             "--stage-id",
             str(stage_id),
@@ -3135,7 +3135,7 @@ def iter_omni_server(
                 raise ValueError("omni_server with use_stage_cli=True requires use_omni=True")
             if stage_config_path is None:
                 raise ValueError("omni_server with use_stage_cli=True requires a stage_config_path")
-            server_args += ["--stage-configs-path", stage_config_path]
+            server_args += ["--deploy-config", stage_config_path]
 
             with OmniServerStageCli(
                 model,
@@ -3151,7 +3151,7 @@ def iter_omni_server(
                 print("OmniServer stopping...")
         else:
             if stage_config_path is not None:
-                server_args += ["--stage-configs-path", stage_config_path]
+                server_args += ["--deploy-config", stage_config_path]
 
             with (
                 OmniServer(

tests/helpers/tests/test_runtime_stage_cli.py

@@ -35,7 +35,7 @@ def test_stage_cli_builds_headless_replica_cmd(tmp_path: Path) -> None:
     assert "--headless" in cmd
     assert cmd[cmd.index("--stage-id") + 1] == "1"
     assert cmd[cmd.index("--replica-id") + 1] == "2"
-    assert cmd[cmd.index("--stage-configs-path") + 1] == server.stage_config_path
+    assert cmd[cmd.index("--deploy-config") + 1] == server.stage_config_path
 
 
 def test_stage_cli_splits_devices_per_replica(tmp_path: Path) -> None:

vllm_omni/deploy/audex_tts_30b.yaml

@@ -17,7 +17,7 @@
 #     measured 0.79% here. The serving layer currently injects 0.05 for
 #     guided requests on all sizes; acceptable (still within the anchor
 #     +2pp standing gate) but a size-aware refinement is a known follow-up.
-# ALWAYS pass this yaml explicitly (--stage-configs-path): bare serve of the
+# ALWAYS pass this yaml explicitly (--deploy-config): bare serve of the
 # 30B repo auto-detects the shared model_type and lands on the 2B-tuned yaml.
 pipeline: audex_tts
 async_chunk: true

vllm_omni/entrypoints/cli/serve.py

@@ -147,7 +147,7 @@ class OmniServeCommand(CLISubcommand):
                 raise ValueError(
                     "The following CLI args are not supported under --omni: "
                     f"{', '.join(offenders)}. Configure parallelism through the "
-                    "per-stage YAML (`--deploy-config` / `--stage-configs-path`) "
+                    "per-stage YAML (`--deploy-config`) "
                     "and replica count via the per-stage `num_replicas` config field "
                     "(single-runtime) or `--omni-dp-size-local` (headless / multi-runtime)."
                 )
@@ -240,20 +240,11 @@ class OmniServeCommand(CLISubcommand):
                 "flag, when set, overrides the YAML model field."
             ),
         )
-        # TODO(@lishunyang12): deprecate once all models migrate to --deploy-config
-        omni_config_group.add_argument(
-            "--stage-configs-path",
-            type=str,
-            default=None,
-            help="[Deprecated — will be removed in a future release] Path to a legacy "
-            "stage configs YAML (stage_args format). Prefer --deploy-config for new-format deploy YAMLs.",
-        )
         omni_config_group.add_argument(
             "--deploy-config",
             type=str,
             default=None,
-            help="Path to a deploy config YAML (new format with stages/engine_args). "
+            help="Path to a deploy config YAML (new format with stages/engine_args).",
-            "Mutually exclusive with --stage-configs-path.",
         )
         omni_config_group.add_argument(
             "--strategy-config",
@@ -846,7 +837,6 @@ def run_headless(args: TrackingNamespace) -> None:
     omni_master_address: str | None = args.omni_master_address
     omni_master_port: int | None = args.omni_master_port
     worker_backend: str | None = args.worker_backend
-    stage_configs_path: str | None = args.stage_configs_path
     omni_replica_address: str | None = getattr(args, "omni_replica_address", None)
     omni_dp_size_local: int = max(1, int(getattr(args, "omni_dp_size_local", 1) or 1))
 
@@ -882,7 +872,8 @@ def run_headless(args: TrackingNamespace) -> None:
 
     config_path, stage_configs, _ = load_and_resolve_stage_configs(
         model,
-        stage_configs_path,
+        # The serve CLI no longer accepts legacy stage_args YAMLs.
+        None,
         args_dict,
         # store_true cannot express an explicit False: absent maps to None
         # ("not specified") so the deploy yaml's per-stage value applies.