Refine the description of docs. (#45)

alibaba · Aug 30, 2024 · b18a315 · b18a315
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diff --git a/docs/en/advanced.rst b/docs/en/advanced.rst
@@ -4,7 +4,7 @@ Advanced Configuration
 StreamDataset
 -------------
 
-The `StreamDataset` receives data generated by the `Env` rollouts and reorganizes it into batches for the `Trainer` training module. Currently, we support three types of `StreamDataset`:
+The `StreamDataset` receives data generated by the `Env` rollouts and reorganizes it into batches for the `Trainer` training module. Currently, we support two types of `StreamDataset`:
 
 1. `fixed`: This type generates a fixed total number of training samples specified by the `sample_per_episode` configuration. The `Env` receives `sample_per_episode` prompts and generates `sample_per_episode` training samples. The `Trainer` then trains on these `sample_per_episode` samples.
 2. `dynamic`: This type generates a dynamically determined total number of training samples. The `Env` receives `sample_per_episode` prompts and generates `N*sample_per_episode` training samples, where `N>0`. The `Trainer` then trains on these `N*sample_per_episode` samples.
@@ -143,7 +143,7 @@ Here is an example of configuring the batch generation optimization. Users can a
 Adaptive checkpoint
 --------------------
 
-In the basic configuration, if different parallel strategies need to be applied to each model of RLHF, the `checkpoint_utils.py` of Megatron-LM needs to be called in advance for offline conversion. Then, the converted checkpoint with the desired parallel strategy can be loaded and the RLHF process can be executed correctly.
+In the basic configuration, if different parallel strategies need to be applied to each model of alignment training, the `checkpoint_utils.py` of Megatron-LM needs to be called in advance for offline conversion. Then, the converted checkpoint with the desired parallel strategy can be loaded and the alignment process can be executed correctly.
 
 In the advanced configuration, adaptive checkpointing is supported, which allows for the automatic loading of checkpoints during the model checkpoint loading process and their conversion to the user-specified parallel strategy. This advanced configuration reduces disk overhead and enables checkpoint conversion to be executed in multiple processes in parallel.
 

diff --git a/docs/zh/advanced.rst b/docs/zh/advanced.rst
@@ -4,7 +4,7 @@
 StreamDataset
 -------------
 
-`StreamDataset` 接收 `Env` rollout 产生的数据，并重组 batch 提供给 Alignment 训练模块 `Trainer`。目前我们支持三种形式的 `StreamDataset`:
+`StreamDataset` 接收 `Env` rollout 产生的数据，并重组 batch 提供给 Alignment 训练模块 `Trainer`。目前我们支持两种形式的 `StreamDataset`:
 
 1. `fixed` ：这种形式生成的总训练样本数是由配置 `sample_per_episode` 指定的。`Env` 接收 `sample_per_episode` 个 prompts，生成 `sample_per_episode` 个训练样本。`Trainer` 接受 `sample_per_episode` 个训练样本进行训练。
 2. `dynamic` : 这种形式生成的总训练样本数是动态判断的。`Env` 接收 `sample_per_episode` 个 prompts，生成 `N*sample_per_episode` 个训练样本，这里 `N>0`。`Trainer` 接受 `N*sample_per_episode` 个训练样本进行训练。
@@ -27,7 +27,7 @@ YAML 配置
 .. csv-table::
    :header: "参数名", "类型", "注释"
 
-   "stream_data_loader_type",               "str",      "指定类型，默认是 fixed，必须是以下三种类型之一，['fixed', 'dynamic']"
+   "stream_data_loader_type",               "str",      "指定类型，默认是 fixed，必须是以下类型之一，['fixed', 'dynamic']"
    "max_relay_episode",               "int",      "指定 relay 的最近的 max_relay_episode 个 episode，超过 max_relay_episode，会淘汰最老的 episode 数据。如果 max_relay_episode 设为 -1，则不会淘汰，记录每个 episode 的历史数据。如果 max_relay_episode 设为 0，则不会开启 relay。"
    "relay_episode_offset",               "int",      "指定从第relay_episode_offset+1个episode开始relay，记录episode 的历史数据。默认为0。"
 
@@ -135,7 +135,7 @@ YAML 配置
 Adaptive checkpoint
 --------------------
 
-基础配置中，如果需要对 RLHF 的各个模型应用不同的并行策略，就要事先调用 Megatron-LM 的 `checkpoint_utils.py` 进行离线转换，然后读取转换并行策略后保存的 checkpoint 才能正常执行 RLHF 流程。进阶配置中，adaptive checkpoint 支持在模型 checkpoint 的加载过程中自适应读取 checkpoint 并自动转换成用户指定的并行策略。该进阶配置相比基础配置可以减少磁盘开销，多进程并行执行 checkpoint 转换。
+基础配置中，如果需要对 Alignment 的各个模型应用不同的并行策略，就要事先调用 Megatron-LM 的 `checkpoint_utils.py` 进行离线转换，然后读取转换并行策略后保存的 checkpoint 才能正常执行 Alignment 流程。进阶配置中，adaptive checkpoint 支持在模型 checkpoint 的加载过程中自适应读取 checkpoint 并自动转换成用户指定的并行策略。该进阶配置相比基础配置可以减少磁盘开销，多进程并行执行 checkpoint 转换。
 
 
 YAML 配置