Simplify batch update (#2154)

docs/backend/backend.md

@@ -79,7 +79,6 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
 ```
-- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currently.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currently.
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.

docs/index.rst

@@ -5,7 +5,7 @@ SGLang is a fast serving framework for large language models and vision language
 It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
 The core features include:
 
-- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
+- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, overhead-free CPU scheduler, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (FP8/INT4/AWQ/GPTQ).
 - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
 - **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models.
 - **Active Community**: SGLang is open-source and backed by an active community with industry adoption.

docs/references/hyperparameter_tuning.md

@@ -31,7 +31,6 @@ If you see out of memory (OOM) errors, you can try to tune the following paramet
 - You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.
 
 ### Try Advanced Options
-- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currently.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currently.
 
 ### Tune `--schedule-policy`