diff --git a/README.md b/README.md
index 610ac07b0..1a8fe56bb 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ NNI automates feature engineering, neural architecture search, hyperparameter tu
## What's NEW! 
-* **New release**: [v2.10 is available](https://github.com/microsoft/nni/releases/tag/v2.10) - _released on Nov-14-2022_
+* **New release**: [v3.0 preview is available](https://github.com/microsoft/nni/releases/tag/v3.0rc1) - _released on May-5-2022_
* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on June-22-2022_
* **New research paper**: [SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute](https://www.usenix.org/system/files/osdi22-zheng-ningxin.pdf) - _published in OSDI 2022_
* **New research paper**: [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf) - _published in CVPR 2022_
diff --git a/docs/source/conf.py b/docs/source/conf.py
index e4b11b8ef..d05a4b2d7 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -31,7 +31,7 @@ author = 'Microsoft'
version = ''
# The full version, including alpha/beta/rc tags
# FIXME: this should be written somewhere globally
-release = 'v2.10'
+release = 'v3.0rc1'
# -- General configuration ---------------------------------------------------
diff --git a/docs/source/release.rst b/docs/source/release.rst
index 06de34358..82eb80b30 100644
--- a/docs/source/release.rst
+++ b/docs/source/release.rst
@@ -5,6 +5,111 @@
Change Log
==========
+Release 3.0 Preview - 5/9/2022
+------------------------------
+
+Web Portal
+^^^^^^^^^^
+
+* New look and feel
+
+Neural Architecture Search
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* **Breaking change**: ``nni.retiarii`` is no longer maintained and tested. Please migrate to ``nni.nas``.
+
+ * Inherit ``nni.nas.nn.pytorch.ModelSpace``, rather than use ``@model_wrapper``.
+ * Use ``nni.choice``, rather than ``nni.nas.nn.pytorch.ValueChoice``.
+ * Use ``nni.nas.experiment.NasExperiment`` and ``NasExperimentConfig``, rather than ``RetiariiExperiment``.
+ * Use ``nni.nas.model_context``, rather than ``nni.nas.fixed_arch``.
+ * Please refer to `quickstart `_ for more changes.
+
+* A refreshed experience to construct model space.
+ * Enhanced debuggability via ``freeze()`` and ``simplify()`` APIs.
+ * Enhanced expressiveness with ``nni.choice``, ``nni.uniform``, ``nni.normal`` and etc.
+ * Enhanced experience of customization with ``MutableModule``, ``ModelSpace`` and ``ParamterizedModule``.
+ * Search space with constraints is now supported.
+
+* Improved robustness and stability of strategies.
+ * Supported search space types are now enriched for PolicyBaseRL, ENAS and Proxyless.
+ * Each step of one-shot strategies can be executed alone: model mutation, evaluator mutation and training.
+ * Most multi-trial strategies now supports specifying seed for reproducibility.
+ * Performance of strategies have been verified on a set of benchmarks.
+
+* Strategy/engine middleware.
+ * Filtering, replicating, deduplicating or retrying models submitted by any strategy.
+ * Merging or transforming models before executing (e.g., CGO).
+ * Arbitrarily-long chains of middlewares.
+
+* New execution engine.
+
+ * Improved debuggability via SequentialExecutionEngine: trials can run in a single process and breakpoints are effective.
+ * The old execution engine is now decomposed into execution engine and model format.
+ * Enhanced extensibility of execution engines.
+
+* NAS profiler and hardware-aware NAS.
+
+ * New profilers profile a model space, and quickly compute a profiling result for a sampled architecture or a distribution of architectures (FlopsProfiler, NumParamsProfiler and NnMeterProfiler are officially supported).
+ * Assemble profiler with arbitrary strategies, including both multi-trial and one-shot.
+ * Profiler are extensible. Strategies can be assembled with arbitrary customized profilers.
+
+Model Compression
+^^^^^^^^^^^^^^^^^
+
+* Compression framework is refactored, new framework import path is ``nni.contrib.compression``.
+
+ * Configure keys are refactored, support more detailed compression configurations.
+ * Support multi compression methods fusion.
+ * Support distillation as a basic compression component.
+ * Support more compression targets, like ``input``, ``ouptut`` and any registered paramters.
+ * Support compressing any module type by customizing module settings.
+
+* Pruning
+
+ * Pruner interfaces have fine-tuned for easy to use.
+ * Support configuring ``granularity`` in pruners.
+ * Support different mask ways, multiply zero or add a large negative value.
+ * Support manully setting dependency group and global group.
+ * A new powerful pruning speedup is released, applicability and robustness have been greatly improved.
+ * The end to end transformer compression tutorial has been updated, achieved more extreme compression performance.
+
+* Quantization
+
+ * Support using ``Evaluator`` to handle training/inferencing.
+ * Support more module fusion combinations.
+ * Support configuring ``granularity`` in quantizers.
+
+* Distillation
+
+ * DynamicLayerwiseDistiller and Adaptive1dLayerwiseDistiller are supported.
+
+* Compression documents now updated for the new framework, the old version please view `v2.10 `_ doc.
+* New compression examples are under `nni/examples/compression `_
+
+ * Create a evaluator: `nni/examples/compression/evaluator `_
+ * Pruning a model: `nni/examples/compression/pruning `_
+ * Quantize a model: `nni/examples/compression/quantization `_
+ * Fusion compression: `nni/examples/compression/fusion `_
+
+Training Services
+^^^^^^^^^^^^^^^^^
+
+* **Breaking change**: NNI v3.0 cannot resume experiments created by NNI v2.x
+* Local training service:
+
+ * Reduced latency of creating trials
+ * Fixed "GPU metric not found"
+ * Fixed bugs about resuming trials
+
+* Remote training service:
+
+ * ``reuse_mode`` now defaults to ``False``; setting it to ``True`` will fallback to v2.x remote training service
+ * Reduced latency of creating trials
+ * Fixed "GPU metric not found"
+ * Fixed bugs about resuming trials
+ * Supported viewing trial logs on the web portal
+ * Supported automatic recover after temporary server failure (network fluctuation, out of memory, etc)
+
Release 2.10 - 11/14/2022
-------------------------