change a bit

docs/机器学习系统/CSE234.en.md

@@ -36,7 +36,7 @@ The course can be more accurately divided into three parts (with several additio
    - LLM fundamentals: Transformers, Attention, and MoE  
    - LLM training optimizations (e.g., FlashAttention-style techniques)  
    - LLM inference: continuous batching, paged attention, disaggregated prefill/decoding  
-   - Scaling laws, test-time compute and reasoning, and “LLM + X” applications (RAG, search, multimodality, tool use, agents, etc.)
+   - Scaling laws
 
 (Guest lectures cover topics such as ML compilers, LLM pretraining and open science, fast inference, and tool use and agents, serving as complementary extensions.)
 

docs/机器学习系统/CSE234.md

@@ -38,7 +38,7 @@
    - LLM 基础：Transformer、Attention、MoE
    - LLM 训练优化：FlashAttention 等
    - LLM 推理：continuous batching、paged attention、disaggregated prefill/decoding
-   - Scaling law、test-time compute / reasoning，以及 “LLM + X”（RAG / search / multimodality / tool-use / agents 等）
+   - Scaling law
 
 （Guest lectures：ML compiler、LLM pretraining/open science、fast inference、tool use & agents 等，作为补充与扩展。）