update docs (#1211)

Author: zhuzilin

README.md

@@ -10,7 +10,7 @@
 1.  **High-Performance Training**: Supports efficient training in various modes by connecting Megatron with SGLang;
 2.  **Flexible Data Generation**: Enables arbitrary training data generation workflows through custom data generation interfaces and server-based engines.
 
-slime is the RL-framework behind [GLM-4.5](https://z.ai/blog/glm-4.5) and [GLM-4.6](https://z.ai/blog/glm-4.6) and apart from models from Z.ai, we also supports the following models:
+slime is the RL-framework behind [GLM-4.7](https://z.ai/blog/glm-4.7), [GLM-4.6](https://z.ai/blog/glm-4.6), [GLM-4.5](https://z.ai/blog/glm-4.5) and apart from models from Z.ai, we also supports the following models:
 - Qwen3 series (Qwen3Next, Qwen3MoE, Qwen3), Qwen2.5 series;
 - DeepSeek V3 series (DeepSeek V3, V3.1, DeepSeek R1);
 - Llama 3.

README_zh.md

@@ -10,7 +10,7 @@
 1. **高性能训练**：通过连接 Megatron 与 SGLang，支持各种模式的高效训练；
 2. **灵活的数据生成**：通过自定义数据生成接口以及 server based engine，实现任意的数据训练数据生成流程。
 
-slime 是 [GLM-4.5](https://z.ai/blog/glm-4.5) 与 [GLM-4.6](https://z.ai/blog/glm-4.6) 背后的 RL 训练框架，除此之外，slime 还支持:
+slime 是 [GLM-4.7](https://z.ai/blog/glm-4.7)、[GLM-4.6](https://z.ai/blog/glm-4.6)、[GLM-4.5](https://z.ai/blog/glm-4.5) 背后的 RL 训练框架，除此之外，slime 还支持:
 - Qwen3 系列 (Qwen3Next, Qwen3MoE, Qwen3), Qwen2.5 系列；
 - DeepSeek V3 系列 (DeepSeek V3, V3.1, DeepSeek R1)；
 - Llama 3。

docs/en/advanced/pd-disaggregation.md

@@ -0,0 +1,5 @@
+# PD Disaggregation
+
+Slime supports Prefill and Decode disaggregation (PD Disaggregation).
+
+You can set the number of servers used for Prefill by setting the `--prefill-num-servers` argument.

docs/en/advanced/speculative-decoding.md

@@ -4,7 +4,7 @@ Speculative decoding is a key optimization for speeding up rollouts. Instead of
 
 ## Accelerating Inference with Speculative Decoding
 
-For models with MTP layers (e.g., GLM-4.6, DeepSeek-V3/R1), simply add:
+For models with MTP layers (e.g., GLM-4.7, DeepSeek-V3/R1), simply add:
 
 ```bash
 --sglang-speculative-algorithm EAGLE