[Workflow] Cleanup workflow docs
diff --git a/README.rst b/README.rst
index c0e32bbf0cb55..8c04c28ee93b4 100644
--- a/README.rst
+++ b/README.rst
@@ -27,7 +27,7 @@ Ray is packaged with the following libraries for accelerating machine learning w
 As well as libraries for taking ML and distributed apps to production:
 
 - `Serve`_: Scalable and Programmable Serving
-- `Workflows`_: Fast, Durable Application Flows (alpha)
+- `Workflow`_: Fast, Durable Application Flows (alpha)
 
 There are also many `community integrations <https://docs.ray.io/en/master/ray-libraries.html>`_ with Ray, including `Dask`_, `MARS`_, `Modin`_, `Horovod`_, `Hugging Face`_, `Scikit-learn`_, and others. Check out the `full list of Ray distributed libraries here <https://docs.ray.io/en/master/ray-libraries.html>`_.
 
@@ -42,7 +42,7 @@ Install Ray with: ``pip install ray``. For nightly wheels, see the
 .. _`Scikit-learn`: https://docs.ray.io/en/master/joblib.html
 .. _`Serve`: https://docs.ray.io/en/master/serve/index.html
 .. _`Datasets`: https://docs.ray.io/en/master/data/dataset.html
-.. _`Workflows`: https://docs.ray.io/en/master/workflows/concepts.html
+.. _`Workflow`: https://docs.ray.io/en/master/workflows/concepts.html
 .. _`Train`: https://docs.ray.io/en/master/train/train.html
 
 
diff --git a/doc/source/workflows/advanced.rst b/doc/source/workflows/advanced.rst
index 3ed03ae76d643..619fb03fd102d 100644
--- a/doc/source/workflows/advanced.rst
+++ b/doc/source/workflows/advanced.rst
@@ -4,7 +4,7 @@ Advanced Topics
 Workflow task Checkpointing
 ---------------------------
 
-Ray Workflows provides strong fault tolerance and exactly-once execution semantics by checkpointing. However, checkpointing could be time consuming, especially when you have large inputs and outputs for workflow tasks. When exactly-once execution semantics is not required, you can skip some checkpoints to speed up your workflow.
+Ray Workflow provides strong fault tolerance and exactly-once execution semantics by checkpointing. However, checkpointing could be time consuming, especially when you have large inputs and outputs for workflow tasks. When exactly-once execution semantics is not required, you can skip some checkpoints to speed up your workflow.
 
 
 We control the checkpoints by specify the checkpoint options like this:
diff --git a/doc/source/workflows/comparison.rst b/doc/source/workflows/comparison.rst
index 52ae404b2a63e..31f4e20ec475e 100644
--- a/doc/source/workflows/comparison.rst
+++ b/doc/source/workflows/comparison.rst
@@ -3,14 +3,14 @@ API Comparisons
 
 Comparison between Ray Core APIs and Workflows
 ----------------------------------------------
-Workflows is built on top of Ray, and offers a mostly consistent subset of its API while providing durability. This section highlights some of the differences:
+Ray Workflow is built on top of Ray, and offers a mostly consistent subset of its API while providing durability. This section highlights some of the differences:
 
 ``func.remote`` vs ``func.bind``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 With Ray tasks, ``func.remote`` will submit a remote task to run eagerly; ``func.bind`` will generate
 a node in a DAG, it will not be executed until the DAG is been executed.
 
-Under the context of Ray Workflows, the execution of the DAG is deferred until ``workflow.run(dag, workflow_id=...)`` or ``workflow.run_async(dag, workflow_id=...)`` is called on the DAG.
+Under the context of Ray Workflow, the execution of the DAG is deferred until ``workflow.run(dag, workflow_id=...)`` or ``workflow.run_async(dag, workflow_id=...)`` is called on the DAG.
 Specifying the workflow id allows for resuming of the workflow by its id in case of cluster failure.
 
 Other Workflow Engines
diff --git a/doc/source/workflows/concepts.rst b/doc/source/workflows/concepts.rst
index 0135b76852497..3b891e283f7e0 100644
--- a/doc/source/workflows/concepts.rst
+++ b/doc/source/workflows/concepts.rst
@@ -1,33 +1,33 @@
 .. _workflows:
 
-Workflows: Fast, Durable Application Flows
-==========================================
+Ray Workflow: Fast, Durable Application Flows
+=============================================
 
 .. warning::
 
-  Workflows is available as **alpha** in Ray 1.7+. Expect rough corners and for its APIs and storage format to change. Please file feature requests and bug reports on GitHub Issues or join the discussion on the `Ray Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`__.
+  Ray Workflow is available as **alpha** in Ray 1.7+. Expect rough corners and for its APIs and storage format to change. Please file feature requests and bug reports on GitHub Issues or join the discussion on the `Ray Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`__.
 
-Ray Workflows provides high-performance, *durable* application workflows using Ray tasks as the underlying execution engine. It is intended to support both large-scale workflows (e.g., ML and data pipelines) and long-running business workflows (when used together with Ray Serve).
+Ray Workflow provides high-performance, *durable* application workflows using Ray tasks as the underlying execution engine. It is intended to support both large-scale workflows (e.g., ML and data pipelines) and long-running business workflows (when used together with Ray Serve).
 
 .. image:: workflows.svg
 
 ..
   https://docs.google.com/drawings/d/113uAs-i4YjGBNxonQBC89ns5VqL3WeQHkUOWPSpeiXk/edit
 
-Why Workflows?
---------------
+Why Ray Workflow?
+-----------------
 
 **Flexibility:** Combine the flexibility of Ray's dynamic task graphs with strong durability guarantees. Branch or loop conditionally based on runtime data. Use Ray distributed libraries seamlessly within workflow tasks.
 
-**Performance:** Workflows offers sub-second overheads for task launch and supports workflows with hundreds of thousands of tasks. Take advantage of the Ray object store to pass distributed datasets between tasks with zero-copy overhead.
+**Performance:** Ray Workflow offers sub-second overheads for task launch and supports workflows with hundreds of thousands of tasks. Take advantage of the Ray object store to pass distributed datasets between tasks with zero-copy overhead.
 
-**Dependency management:** Workflows leverages Ray's runtime environment feature to snapshot the code dependencies of a workflow. This enables management of workflows and virtual actors as code is upgraded over time.
+**Dependency management:** Ray Workflow leverages Ray's runtime environment feature to snapshot the code dependencies of a workflow. This enables management of workflows as code is upgraded over time.
 
-You might find that workflows is *lower level* compared to engines such as `AirFlow <https://www.astronomer.io/blog/airflow-ray-data-science-story>`__ (which can also run on Ray). This is because workflows focuses more on core workflow primitives as opposed to tools and integrations.
+You might find that Ray Workflow is *lower level* compared to engines such as `AirFlow <https://www.astronomer.io/blog/airflow-ray-data-science-story>`__ (which can also run on Ray). This is because Ray Workflow focuses more on core workflow primitives as opposed to tools and integrations.
 
 Concepts
 --------
-Workflows provides the *task* and *virtual actor* durable primitives, which are analogous to Ray's non-durable tasks and actors.
+Ray Workflow provides the durable *task* primitives, which are analogous to Ray's non-durable tasks.
 
 Ray DAG
 ~~~~~~~
diff --git a/doc/source/workflows/events.rst b/doc/source/workflows/events.rst
index d423b08aa2eec..faea753f8779b 100644
--- a/doc/source/workflows/events.rst
+++ b/doc/source/workflows/events.rst
@@ -4,7 +4,7 @@ Events
 Introduction
 ------------
 
-In order to allow an event to trigger a workflow, workflows support pluggable event systems. Using the event framework provides a few properties.
+In order to allow an event to trigger a workflow, Ray Workflow support pluggable event systems. Using the event framework provides a few properties.
 
 1. Waits for events efficiently (without requiring a running workflow task while waiting).
 2. Supports exactly-once event delivery semantics while providing fault tolerance.
diff --git a/doc/source/workflows/management.rst b/doc/source/workflows/management.rst
index 62cfa8beb392e..8b2996c3e630b 100644
--- a/doc/source/workflows/management.rst
+++ b/doc/source/workflows/management.rst
@@ -76,11 +76,11 @@ Bulk workflow management APIs
 Recurring workflows
 -------------------
 
-Ray workflows currently has no built-in job scheduler. You can however easily use any external job scheduler to interact with your Ray cluster (via :ref:`job submission <jobs-overview>` or :ref:`client connection <ray-client>`) trigger workflow runs.
+Ray Workflow currently has no built-in job scheduler. You can however easily use any external job scheduler to interact with your Ray cluster (via :ref:`job submission <jobs-overview>` or :ref:`client connection <ray-client>`) trigger workflow runs.
 
 Storage Configuration
 ---------------------
-Workflows supports two types of storage backends out of the box:
+Ray Workflow supports two types of storage backends out of the box:
 
 *  Local file system: the data is stored locally. This is only for single node testing. It needs to be a NFS to work with multi-node clusters. To use local storage, specify ``ray.init(storage="/path/to/storage_dir")``.
 *  S3: Production users should use S3 as the storage backend. Enable S3 storage with ``ray.init(storage="s3://bucket/path")``.
@@ -91,7 +91,7 @@ If left unspecified, ``/tmp/ray/workflow_data`` will be used for temporary stora
 
 Concurrency Control
 -------------------
-Ray workflow supports concurrency control. You can support the maximum running workflows and maximum pending workflows via ``workflow.init()``
+Ray Workflow supports concurrency control. You can support the maximum running workflows and maximum pending workflows via ``workflow.init()``
 before executing any workflow. ``workflow.init()`` again with a different configuration would raise an error.
 
 For example, ``workflow.init(max_running_workflows=10, max_pending_workflows=50)`` means there will be at most 10 workflows running, 50 workflows pending.
@@ -112,5 +112,3 @@ Handling Dependencies
 Ray logs the runtime environment (code and dependencies) of the workflow to storage at submission time. This ensures that the workflow can be resumed at a future time on a different Ray cluster.
 
 You can also explicitly set the runtime environment for a particular task (e.g., specify conda environment, container image, etc.).
-
-For virtual actors, the runtime environment of the actor can be upgraded via the virtual actor management API.
diff --git a/doc/source/workflows/package-ref.rst b/doc/source/workflows/package-ref.rst
index 44a88f41ca1b0..089f76c0a7b4e 100644
--- a/doc/source/workflows/package-ref.rst
+++ b/doc/source/workflows/package-ref.rst
@@ -1,4 +1,4 @@
-Ray Workflows API
+Ray Workflow API
 =================
 
 Workflow Execution API

Signed-off-by: Siyuan Zhuang <suquark@gmail.com>

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Cherry pick #27197

## Why are these changes needed?

* Unify the component name - "Ray Workflow" instead of "workflow", "workflows", "Ray workflow", "Ray workflows", "Ray Workflows"
* Remove remaining part about virtual actors

## Related issue number

Closes #26971

## Checks

- [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR.
- [ ] I've run `scripts/format.sh` to lint the changes in this PR.
- [ ] I've included any doc changes needed for https://docs.ray.io/en/master/.
- [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
   - [ ] Unit tests
   - [ ] Release tests
   - [ ] This PR is not tested :(
