diff --git a/README.md b/README.md
index 53d6a956..c8521ea9 100644
--- a/README.md
+++ b/README.md
@@ -31,9 +31,9 @@ TransferQueue offers **fine-grained, sample-level** data management and **load-b
🔄 Updates
- **Dec 30, 2025**: **TransferQueue x verl** integration is tested with the DAPO algorithm at scale **(64 nodes, 1024 cards)**. It significantly optimizes host memory utilization and accelerates data transfers. Stay tuned for more details!
- - **Dec 20, 2025**: 🔥 The official [tutorial](https://github.com/TransferQueue/TransferQueue/tree/main/tutorial) is released! Feel free to check it out.
+ - **Dec 20, 2025**: 🔥 The official [tutorial](https://github.com/Ascend/TransferQueue/tree/main/tutorial) is released! Feel free to check it out.
- **Nov 10, 2025**: We disentangle the data retrieval logic from TransferQueueController [PR#101](https://github.com/TransferQueue/TransferQueue/pull/101). Now you can implement your own `Sampler` to control how to consume the data.
- - **Nov 5, 2025**: We provide a `KVStorageManager` that simplifies the integration with KV-based storage backends [PR#96](https://github.com/TransferQueue/TransferQueue/pull/96). The first available KV-based backend is [Yuanrong](https://gitee.com/openeuler/yuanrong-datasystem).
+ - **Nov 5, 2025**: We provide a `KVStorageManager` that simplifies the integration with KV-based storage backends [PR#96](https://github.com/TransferQueue/TransferQueue/pull/96). The first available KV-based backend is [Yuanrong](https://gitcode.com/openeuler/yuanrong-datasystem).
- **Nov 4, 2025**: Data partition capability is available in [PR#98](https://github.com/TransferQueue/TransferQueue/pull/98). Now you can define logical data partitions to manage your train/val/test datasets.
- **Oct 25, 2025**: We make storage backends pluggable in [PR#66](https://github.com/TransferQueue/TransferQueue/pull/66). You can try to integrate your own storage backend with TransferQueue now!
- **Oct 21, 2025**: Official integration into verl is ready [verl/pulls/3649](https://github.com/volcengine/verl/pull/3649). Following PRs will optimize the single controller architecture by fully decoupling data & control flows.
@@ -114,7 +114,7 @@ Core interfaces:
- `(async_)put(data: TensorDict, metadata: Optional[BatchMeta], partition_id: Optional[str])`
- `(async_)clear_partition(partition_id: str)` and `(async_)clear_samples(metadata: BatchMeta)`
-**Refer to our [tutorial](https://github.com/TransferQueue/TransferQueue/tree/main/tutorial) for detailed examples.**
+**Refer to our [tutorial](https://github.com/Ascend/TransferQueue/tree/main/tutorial) for detailed examples.**
### Collocated Example
@@ -131,7 +131,7 @@ Leveraging TransferQueue, we separate experience data transfer from metadata dis

-You may refer to the [recipe](https://github.com/TransferQueue/TransferQueue/tree/dev/recipe/simple_use_case), where we mimic the verl usage in both async & sync scenarios. Official integration to verl is also available now at [verl/pulls/3649](https://github.com/volcengine/verl/pull/3649) (with subsequent PRs to further optimize the integration).
+You may refer to the [recipe](https://github.com/Ascend/TransferQueue/tree/dev/recipe/simple_use_case), where we mimic the verl usage in both async & sync scenarios. Official integration to verl is also available now at [verl/pulls/3649](https://github.com/volcengine/verl/pull/3649) (with subsequent PRs to further optimize the integration).
### Disaggregated Example
@@ -153,7 +153,7 @@ pip install TransferQueue
Follow these steps to build and install:
1. Clone the source code from the GitHub repository
```bash
- git clone https://github.com/TransferQueue/TransferQueue/
+ git clone https://github.com/Ascend/TransferQueue/
cd TransferQueue
```
@@ -239,7 +239,7 @@ batch_meta = client.get_meta(
)
```
-**Refer to [tutorial/04_custom_sampler.py](https://github.com/TransferQueue/TransferQueue/blob/main/tutorial/04_custom_sampler.py) for more details.**
+**Refer to [tutorial/04_custom_sampler.py](https://github.com/Ascend/TransferQueue/blob/main/tutorial/04_custom_sampler.py) for more details.**
### How to integrate a new storage backend