[doc]fix: optimize ascend docs (#3063)

### What does this PR do?

- 修复ascend_quick_start.rst中一些依赖软件的版本匹配错误。
- 支持现状表格中增加对actor.strategy和rollout.name的说明。
- 重命名ascend_profiling_en.rst和ascend_profiling_zh.rst，使文档标题看起来更美观些。 
<img width="402" height="103" alt="image"
src="https://github.com/user-attachments/assets/8f9ece22-315e-4f80-8157-04838f7467a3"
/>

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Author: zheliuyu

docs/ascend_tutorial/ascend_profiling_en.rst

@@ -1,4 +1,4 @@
-Data collection based on FSDP (Fully Sharded Data Parallel) backend on Ascend devices(NPU)
+Data collection based on FSDP backend on Ascend devices(en)
 ==========================================================================================
 
 Last updated: 07/24/2025.

docs/ascend_tutorial/ascend_profiling.rst renamed to docs/ascend_tutorial/ascend_profiling_zh.rst

@@ -1,6 +1,8 @@
-在昇腾设备上基于FSDP后端进行数据采集
+Data collection based on FSDP backend on Ascend devices(zh)
 ====================================
 
+在昇腾设备上基于FSDP后端进行数据采集
+
 Last updated: 07/24/2025.
 
 这是一份在昇腾设备上基于FSDP后端使用GRPO或DAPO算法进行数据采集的教程。

docs/ascend_tutorial/ascend_quick_start.rst

@@ -1,7 +1,7 @@
 verl x Ascend
 ===================================
 
-Last updated: 06/17/2025.
+Last updated: 08/15/2025.
 
 我们在 verl 上增加对华为昇腾设备的支持。
 
@@ -28,9 +28,10 @@ Atlas 900 A2 PODc
 +-----------+-------------+
 | torch     | == 2.5.1    |
 +-----------+-------------+
-| torch_npu | == 2.5.1.RC1|
+| torch_npu | == 2.5.1    |
 +-----------+-------------+
 
+基础环境准备请参照这份 `文档 <https://gitee.com/ascend/pytorch>`_ 。
 
 vllm & vllm-ascend
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -80,14 +81,11 @@ vllm & vllm-ascend
 +--------------+---------------+
 | liger-kernel | not supported |
 +--------------+---------------+
-| tensordict   | 0.8.3 (ARM)   |
-+--------------+---------------+
 
-1. 支持通过 transformers 使能 --flash_attention_2， transformers 需大于等于 4.52.0版本。
+1. 支持通过 transformers 使能 --flash_attention_2， transformers 需等于 4.52.4版本。
 2. 不支持通过 flash_attn 使能 flash attention 加速。
 3. 不支持 liger-kernel 使能。
-4. 针对 ARM 服务器，tensordict 要求 0.8.3，可在依赖安装完成后再手动安装 tensordict。
-5. 针对 x86 服务器，需要安装 cpu 版本的 torchvision。
+4. 针对 x86 服务器，需要安装 cpu 版本的 torchvision。
 
 .. code-block:: bash
 
@@ -153,50 +151,50 @@ vllm & vllm-ascend
         trainer.total_epochs=1 \
         trainer.device=npu $@
 
-MindSpeed 训练后端
+(可选) 设置MindSpeed训练后端指导
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-1. 参考 `MindSpeed Readme <https://gitee.com/ascend/MindSpeed>`_ 说明安装 MindSpeed 加速库。
+1. 参考 `MindSpeed README <https://gitee.com/ascend/MindSpeed>`_ 说明安装 MindSpeed 加速库。
 
-2. 使能 Verl worker 模型 ``strategy`` 配置为 ``megatron`` ，例如 ``actor_rollout_ref.actor.strategy=megatron``。
+2. 使能 verl worker 模型 ``strategy`` 配置为 ``megatron`` ，例如 ``actor_rollout_ref.actor.strategy=megatron``。
 
 3. MindSpeed 自定义入参可通过 ``override_transformer_config`` 参数传入，例如对 actor 模型开启 FA 特性可使用 ``+actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True``。
 
-4. 更多特性信息可参考 `MindSpeed Verl 文档 <https://gitee.com/ascend/MindSpeed/blob/master/docs/user-guide/verl.md>`_ 。
+4. 更多特性信息可参考 `MindSpeed+verl 文档 <https://gitee.com/ascend/MindSpeed/blob/master/docs/user-guide/verl.md>`_ 。
 
 支持现状
 -----------------------------------
 
 **表1** RL类算法
 
-+-----------+-------------------------+-------------+-------------------+----------------------+
-| algorithm |         model           | rewards mae |  throughput ratio |        hardware      |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   GRPO    | Qwen2.5-7B-instruct     |    0.38%    |        0.588      |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   GRPO    | Qwen2.5-32B-instruct    |    0.30%    |        0.685      |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   GRPO    | Qwen2.5-VL-3B-instruct  |    3.14%    |        0.470      |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   GRPO    | Qwen2.5-VL-7B-instruct  |    3.30%    |        0.380      |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   GRPO    | Qwen2.5-VL-32B-instruct |    0.79%    |        0.568      |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   DAPO    | Qwen2.5-7B-instruct     |    3.83%    |        pending    |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   DAPO    | Qwen3-8B-base           |    5.3%     |        pending    |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
-|   DAPO    | Qwen3-14B-base          |    5.9%     |        pending    |  Atlas 200T A2 Box16 |
-+-----------+-------------------------+-------------+-------------------+----------------------+
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
+| algorithm |         model           | rewards mae |  throughput ratio |   actor.strategy  |   rollout.name    |         hardware         |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   GRPO    | Qwen2.5-7B-instruct     |    0.38%    |        0.588      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   GRPO    | Qwen2.5-32B-instruct    |    0.30%    |        0.685      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   GRPO    | Qwen2.5-VL-3B-instruct  |    3.14%    |        0.470      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   GRPO    | Qwen2.5-VL-7B-instruct  |    3.30%    |        0.380      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   GRPO    | Qwen2.5-VL-32B-instruct |    0.79%    |        0.568      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------|
+|   DAPO    | Qwen2.5-7B-instruct     |    3.83%    |        pending    |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
+|   DAPO    | Qwen3-8B-base           |    5.3%     |        pending    |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
+|   DAPO    | Qwen3-14B-base          |    5.9%     |        pending    |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
 
 **表2** SFT类算法
 
-+-----------+-------------------------+----------------+-------------------+----------------------+
-| algorithm |         model           | loss value mae |  total time ratio |        hardware      |
-+-----------+-------------------------+----------------+-------------------+----------------------+
-|  SFT-PEFT | Qwen3-8B                |      0.09%     |       0.618       |  Atlas 900 A2 PODc   |
-+-----------+-------------------------+----------------+-------------------+----------------------+
-| ReTool-SFT| Qwen2.5-7B-instruct     |      0.08%     |       0.775       |  Atlas 900 A2 PODc   |
-+-----------+-------------------------+----------------+-------------------+----------------------+
++-----------+-------------------------+----------------+-------------------+-------------------+----------------------+
+| algorithm |         model           | train loss mae |  total time ratio |   actor.strategy  |        hardware      |
++-----------+-------------------------+----------------+-------------------+-------------------+----------------------+
+|  SFT-PEFT | Qwen3-8B                |      0.09%     |       0.618       |        FSDP       |   Atlas 900 A2 PODc  |
++-----------+-------------------------+----------------+-------------------+-------------------+----------------------+
+| ReTool-SFT| Qwen2.5-7B-instruct     |      0.08%     |       0.775       |        FSDP       |   Atlas 900 A2 PODc  |
++-----------+-------------------------+----------------+-------------------+-------------------+----------------------+
 
 精度对比说明
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -217,10 +215,10 @@ Ascend npu 和 A100 分别取日志中前4个 step 的 "perf/throughput" 做平
 计划
 -----------------------------------
 
-查看 `roadmap <https://github.com/volcengine/verl/discussions/900>`_ 获取更多特性的支持进度。
+查看 `roadmap <https://github.com/volcengine/verl/discussions/2171>`_ 获取更多特性的支持进度。
 
 
 
 声明
 -----------------------------------
-verl中提供的ascend支持代码皆为参考样例，商业使用请通过官方正式途径沟通，谢谢。
+verl中提供的ascend支持代码皆为参考样例，如在生产环境中使用请通过官方正式途径沟通，谢谢。